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Int. J. Metadata, Semantics and Ontologies, Vol. 5, No. 1, 2010
Ontological technologies for user modelling
Sergey Sosnovsky*
School of Information Sciences,
University of Pittsburgh,
Pittsburgh, PA 15260, USA
E-mail: sosnovsky@gmail.com
*Corresponding author
Darina Dicheva
Department of Computer Science,
Winston-Salem State University,
Winston Salem, NC 27110, USA
E-mail: dichevad@wssu.edu
Abstract: This paper brings together research from two different fields – user modelling and
web ontologies – in attempt to demonstrate how recent semantic trends in web development
can be combined with the modern technologies of user modelling. Over the last several years,
a number of user-adaptive systems have been exploiting ontologies for the purposes of semantics
representation, automatic knowledge acquisition, domain and user model visualisation and
creation of interoperable and reusable architectural solutions. Before discussing these projects,
we first overview the underlying user modelling and ontological technologies. As an example
of the project employing ontology-based user modelling, we present an experiment design for
translation of overlay student models for relative domains by means of ontology mapping.
Keywords: user modelling; ontologies; adaptation; SW; semantic web.
Reference to this paper should be made as follows: Sosnovsky, S. and Dicheva, D. (2010)
‘Ontological technologies for user modelling’, Int. J. Metadata, Semantics and Ontologies,
Vol. 5, No. 1, pp.32–71.
Biographical notes: Sergey Sosnovsky is a PhD candidate at the School of Information
Sciences, University of Pittsburgh, Pittsburgh, PA, USA. He received his MSc and BSc from
Kazan State Technological University, Kazan, Russia. His research focuses on combining
new trends of Web development with Adaptive and User Modelling technologies. He has
co-authored about 60 peer-reviewed research publications and served on programming
committees of several workshops in the area of Semantic Web for Adaptation.
Darina Dicheva is Paul Fulton Distinguished Professor of Computer Science at Winston-Salem
State University, NC, USA. Her research interests include semantic web, social networks,
information retrieval and filtering, user modelling, intelligent learning environments, and digital
libraries. She has authored over 120 research papers and has been on the programme committees
of more than 40 international conferences in the recent years. She is a co-organiser of the
International Workshop on Semantic Web for e-Learning (SWEL) series.
1
Introduction
Among the promising directions of the World Wide Web
(WWW) development, two paradigms can be named that
share the ultimate goal of providing users with more
efficient access to online information and services: the
Adaptive Web (AW) and the Semantic Web (SW). The first
aims at enabling different kinds of personalised information
experiences from adaptive e-shopping to online intelligent
tutoring. The second proposes a set of standards and
technologies for meaningful (semantically-enriched)
description and automatic discovery of data and knowledge
on the web. Bringing these two fields together can result
Copyright © 2010 Inderscience Enterprises Ltd.
in a successful, mutually beneficial synergy. The AW
challenges introduce interesting application areas for the
SW technologies, while the SW vision, in its turn, opens
new perspectives for the AW research.
In this review we do not attempt to describe all aspects
of the AW-SW technology fusion, instead, we focus on
analysing the novel projects emerged at the border of two
core subfields of AW and SW: user modelling and web
ontologies.
User modelling is much an older area of research
than AW. The first systems modelling users were developed
as long ago as 1970s. Nowadays, user modelling constitutes
one of the major and perhaps the most challenging
Ontological technologies for user modelling
part of the development of AW applications. The precision
of the modelling assumptions about the users defines the
effectiveness of adaptive systems in general. An incorrect
interpretation of a user leads to wrong adaptive decisions,
which may result in user’s frustration, loss of trust,
decrease in motivation to use the system, etc. Adequate
representation of knowledge about the users, effective
elicitation of user-related information, and utilisation of
this information for organising coherent and meaningful
adaptation are crucial factors for the success of AW
systems.
The notion of web ontology is central for the SW
initiative. Automatic discovery of information on the web
and its machine-based interpretation are not possible unless
the information is semantically-enriched with metadata
providing the shared meaning of the content. Ontologies are
the instrument to present and convey such meaning.
Ontologies have been studied for many years, first by
philosophers and logicians, and later by researchers in
the field of Artificial Intelligence (AI) and Knowledge
Representation (KR). SW, however, gave a new spin to this
study and nowadays the field of web ontologies is attracting
much of interest. The SW activity of the World Wide Web
Consortium (W3C) leads and supports these efforts by
developing a set of standards for ontology representation
and processing on the web (W3C, 2001b).
Recently a lot of research has been done on the
crossroad of user modelling and web ontologies. Ultimately,
both disciplines attempt to model real world phenomena
qualitatively: ontologies – a particular area of knowledge,
and user modelling – the internal state of a human user.
Many user-modelling approaches exploit content-based
characteristics of users (users’ knowledge, interests, etc.)
and hence can directly benefit of the high-quality domain
models provided by ontologies. Besides, as the majority of
user modelling projects have been deployed on the web
and web ontologies are becoming de facto standard for
WWW-based KR, the cooperation between user modelling
and web ontologies seems inevitable.
The paper is organised as follows. The next section
gives an overview of user modelling. It starts with a
description of different user’s characteristics modelled
by adaptive systems, followed by an analysis of the most
important technologies for user model representation and
elicitation. The set of technologies is chosen to demonstrate
the applicability of ontologies to user modelling.
Section 3 is devoted to ontologies. It first discusses
the origins of the ontological research and the requirements
for web ontologies and then summarises central aspects
of ontologies, including ontology representation,
ontology-based knowledge acquisition, ontology-based
annotation, ontology mapping, etc.
The fourth and central section examines a set of projects
that merge user modelling and ontologies. It discusses such
features of ontologies as controlled vocabulary of terms,
available formats for sharable knowledge representation,
well-defined structure of domain knowledge, support of
logical reasoning, etc., that have been appreciated by the
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UM researchers and utilised for developing AW systems.
The section attempts to draw the connection between certain
technologies, for example, open user modelling and
ontology visualisation, overlay user modelling and ontology
representation, unobtrusive user modelling and ontology
learning, etc. As this area of research is rather new,
the section may look unbalanced. Some of its subsections
are more detailed than others; for some problems solutions
have not been proposed yet. More research efforts are
needed for ontology-based user modelling technologies to
mature.
Section 5 concludes the paper by mentioning not
covered issues and technologies, as well as possible future
trends.
2
User modelling
The field of user modelling came a long way from lab
projects (Carbonell, 1970) to the development of dedicated
commercial user modelling servers that support millions of
users (see Fink and Kobsa, 2000) for a comprehensive
review). This section provides a brief overview of the
evolution of efforts in this field and lists the most important
user modelling technologies developed over the years.
It focuses on the user modelling trends that can benefit of
integration with the field of web ontologies.
2.1 User modelling dimensions
The core idea of adaptation is based on the assumption that
differences in some user characteristics affect the usefulness
of the services or information provided to the individuals.
Thus if a system’s behaviour is tailored according to such
characteristics, its value to individuals will be increased.
This section describes the most important characteristics
with regard to adaptation.
2.1.1 Knowledge, beliefs, and background
These characteristics are especially important for the
adaptive systems modelling students. Among all kinds of
user-adaptive systems, Adaptive Educational Systems
(AES) have the longest history of research. This class of
adaptive systems is the most diverse and numerous.
To name a few, it includes ICAI systems (e.g., Scholar
(Carbonell, 1970), Cognitive Tutors (e.g., Lisp-Tutor
(Corbett and Anderson, 1994)), and Adaptive Educational
Hypermedia Systems (e.g., Interbook (Brusilovsky et al.,
1996)). For any of these systems student’s knowledge is the
main characteristic defining system’s adaptivity. The most
popular approach to modelling student knowledge relies on
a fine-grained conceptual structure of a learning domain;
thus, the aggregate student model consists of indicators for
the student’s knowledge of particular domain concepts.
Different systems manage differently the evidence of
incorrect knowledge or misconceptions. Some AESs simply
accumulate this negative evidence in the knowledge
assessment for corresponding concepts (Galeev et al., 1994)
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S. Sosnovsky and D. Dicheva
or simply mark concepts as having a misconception
(Mabbott and Bull, 2004), other systems support a special
‘buggy model’ (Vassileva, 1997). Some authors argue that
the student model of an AES should not directly
differentiate between correct and incorrect knowledge of
a concept and treat them instead as student beliefs
(Self, 1988).
Another relevant characteristic of a student is her or his
background. Traditionally, background is defined as
relevant experience gained outside the system, prior to using
it. Unlike knowledge models, a background model is usually
static (it does not change over time) and coarse-grained.
Most typically, it is represented as a single parameter with
several possible values (e.g., ‘novice’, ‘advanced’, ‘expert’)
(Horvitz et al., 1998) or as a set of stereotypes (Rich, 1979).
2.1.2 Interests and preferences
These two characteristics have been often used as
synonyms, especially in the context of AW systems. Several
types of adaptive systems have been developed to assist
information harvesting on the web, including adaptive
recommenders (Pazzani and Billsus, 2007), adaptive search
engines (Micarelli et al., 2007), and adaptive browser
agents (Lieberman, 1995). The most important user
characteristics for such systems are user’s information
preferences or information interests. When modelling users’
preferences/interests the systems typically distinguish
between long-term and short-term models. Long-term
interests are relatively stable; they evolve slowly along the
entire period of user’s working with the system, or are
provided by the user explicitly in the form of general
categories. A short-term model of interests is dynamic and
usually populated and used during a single session,
reflecting user’s current information task. The short-term
model is discarded when the session ends.
2.1.3 Goals, plans, tasks and needs
Modelling users’ goals and plans has been widely exploited
in intelligent dialog systems. Knowledge of what situation a
user is trying to achieve (goal) and what sequence of actions
she or he is going to take on the way to the desired state of
affairs (plan) is essential for such systems to maintain
adaptive conversation with the user (Kass and Finin, 1988).
Very close to these modelling dimensions is the concept
of task or need. Modelling users’ information needs or
information seeking tasks is a very popular approach
employed by different adaptive systems on the web, such as
adaptive recommender systems (McNee et al., 2006).
AES model a student’s goal/task from a different
perspective. Since the general student’s goal is clear – to
learn the material – they do not try to recognise it but aim at
finding the best strategy leading to the most efficient
learning (see, for example, Conati et al. (1997)).
2.1.4 Demographic information
For some kinds of adaptive systems, it is vital to be aware of
demographic characteristics of a user from the very basic
features, like gender, age or native language to more
complex socio-cultural parameters, such as level of formal
education and family income. Adaptation to demographic
information is widely used in adaptive e-commerce systems
(Bowne, 2000) and personalised ubiquitous applications
(Fink and Kobsa, 2002). It can be also important in
educational setting (Desimone, 1999).
2.1.5 Emotional state
Recently, a new class of technologies combined under the
name ‘affective computing’ has gained a lot of interest
in the field of user modelling (Picard and Klein, 2002).
An adaptive system capable of modelling a certain user’s
emotions obtains one more source for proper tailoring and
justification of its behaviour. For example, the authors of
Rodrigo et al. (2007) have found that such emotions as
boredom and confusion lead students to an off-task
behaviour, namely, gaming the adaptive system. One of the
main challenges in affective computing is how to recognise
different kinds of user’s emotions (Picard, 2003).
2.1.6 Context
Users’ context modelling is becoming an important
direction of research as the interest in the development of
adaptive applications for pervasive and ubiquitous settings
is growing. The context of a user is a very broad concept;
it can include any information about a user’s location, time,
physical and social environment, the device being used to
access the system, etc. Currently, the most popular class of
applications adapting to the user’s context is various kinds
of personalised guides and tours. For example, the GUIDE
system (Cheverst et al., 2000) creates an adaptive tour of the
city of Lancaster by utilising the current location of the user
(to navigate to the most appropriate city attraction) and
the time of the day (to match the hours when the attractions
are open for visitors).
2.2 User model representation
This section discusses how the selected characteristics of a
user can be modelled. Two classic approaches – overlay
user modelling and user modelling via stereotypes – are
presented. In addition, we describe the keyword-based
representation of user models, which became popular as the
web-based adaptive information retrieval technologies
matured. Finally, we briefly summarise other important
technologies for user model representations, which are less
relevant to the main topic of this survey.
2.2.1 Overlay user modelling
This is the oldest approach to the user model representation.
Traditionally, it was employed by different kinds of AES for
modelling student knowledge as a subset of domain expert
knowledge. In their classic paper, Carr and Goldstein coined
the term and provided the first definition of overlay user
modelling, as a technique for “describing a student’s skills”
Ontological technologies for user modelling
as “a set of hypotheses regarding the student’s familiarity”
with the elements of domain representation provided by an
expert (Carr and Goldstein, 1977). However, the main idea
of graining the domain of discourse into elementary
components and using them for evaluation of students’
knowledge was first proposed in the Scholar system
(Carbonell, 1970). These components have been named
differently by different authors: topics, knowledge elements,
learning outcomes, and – the most widely used – concepts.
In this context, a concept represents an atomic piece of
declarative domain knowledge, coherent and semantically
complete. An aggregate of concepts form the domain model.
The overlay user model relies on the domain model as on a
template and, essentially, consists of a set of concept-value
pairs, where the value represents an assessment of the
particular concept. Some systems also apply the overlay
approach to representing user preferences or interests
(e.g., De Bra et al., 2003).
The benefit of the overlay user model is its precision and
flexibility. Fine-grained concept-based modelling allows
systems to adjust their actions on a very detailed level.
An overlay model is capable to dynamically and precisely
reflect the evolution of users’ characteristics (which is
especially important for AES). Among the drawbacks of
this approach is the necessity of developing an accurate and
formal domain model, which is a hard task for some
domains.
2.2.2 Keyword-based user modelling
Keyword-based user modelling originated in the areas of
information retrieval and filtering (Belkin and Croft, 1992),
where the content of a document is traditionally represented
as a vector of terms (keywords) extracted from the text.
The adaptive information retrieval and filtering applications
aggregate a user’s history of launched queries, accessed
documents, or rejected e-mails in a form of a keyword
vector and use this vector for tailoring a future retrieval or
filtering process to the user’s information-seeking
idiosyncrasies.
To a certain extent, this approach can be considered a
shallow version of overlay user modelling. It also utilises
elements of domain representation as a frame of reference to
express user characteristics on the atomic level. However,
instead of concepts modelling domain semantics, this
technique uses keywords/terms found in the content.
It became very popular in the context of adaptive
information retrieval on the web (Brusilovsky and Tasso,
2004). Many AW systems model users’ information
interests or needs as vectors of keywords extracted from the
documents the users have browsed or requested, as for
example in Letizia (Lieberman, 1995), WebMate (Chen and
Sycara, 1998), NewsDude (Billsus and Pazzani, 1999), etc.
Some of the systems model users’ interests as networks of
keywords instead of plain lists, where nodes represent
keywords and arcs connect keywords co-occurring in the
content, as in ifWeb (Asnicar and Tasso, 1997) and PIN
(Tan and Teo, 1998).
35
A big advantage of this approach is the automatic
modelling of content based on well-developed IR
techniques for text analysis, which also opens opportunities
for open-corpus adaptation. However, keywords support
only shallow content models. To remedy problems like
homonymy (multiple meanings of a word) and synonymy
(multiple words expressing the same meaning), Natural
Language (NL) technologies are required. Pure keywordbased modelling is not able to represent the true meaning of
the content. It relies on statistical regularities within the
text and, essentially, provides a framework for retrieving
statistically close documents.
2.2.3 Stereotype user modelling
The ultimate goal of adaptive systems is to best adjust
its behaviour to the characteristics of individual users.
However, for some tasks it is possible to identify typical
categories of users that use the system in a similar way,
expect from it similar outcomes and can be described by
similar sets of features. Such categories “constituting strong
points of commonalities” (Kay, 1994) among users are
called stereotypes.
An adaptive system relying on stereotype-based
modelling does not update every single facet of the
user model directly. Instead, it utilises a stock of preset
stereotype profiles. Whenever the system receives an
evidence of a user being characterised by a certain
stereotype, the entire user model is updated with the
information from that stereotype profile. A user can be
described by one stereotype or a combination of several
orthogonal stereotypes. A popular way of stereotype-based
user modelling is a linear set of categories for representing
typical levels of user proficiency. For example, Chin (1989)
describes the KNOME system modelling users’ expertise
of the UNIX operating system on one of four levels
(novice, beginner, intermediate, and expert). Often,
however, stereotypes form a hierarchy, where more specific
stereotypes can inherit some information from their parents,
as in the classic system Grundy (Rich, 1979).
Stereotype-based user modelling is advantageous
when from a little evidence about a user the system should
infer a great deal of modelling information. However,
for modelling fine-grained characteristics of individual users
(for example, a knowledge level of a particular concept)
more precise overlay models should be employed.
2.2.4 Other technologies for user model
representation
We have described these three approaches in details due to
the many projects employing the synergy between them and
the web ontologies. However, other important technologies
for user model representation should be mentioned as well.
Constrained-based user modelling has been successfully
used for modelling student’s knowledge in Intelligent
Tutoring Systems (ITSs) (Mitrovic et al., 2001). In this
approach every constraint represents an acceptable set of
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S. Sosnovsky and D. Dicheva
equivalent problem states and a violated constraint indicates
an error.
One of the dominating adaptive technologies on the web
nowadays is collaborative filtering. Unlike most of the
user modelling technologies mentioned before it relies on
modelling users in terms of their relationships with other
users. The typical collaborative user model is based on a
vector of ratings the user provided for particular items
(Konstan et al., 1997).
Bayesian Networks is yet another very popular
formalism for representing different aspects of user models.
Multiple systems used Bayesian Networks to model the
relations between different components/dimensions of a
user model, such as emotions, goals and knowledge
(e.g., Zhou and Conati, 2003). Other systems used them to
implement an overlay user model with internal inference
capabilities, where every node represents a domain concept
and links stay for the concept relations, e.g., de Rosis et al.
(1992).
In the e-commerce applications it is often effective to
model a customer without deriving any explicit modelling
assumptions about him, but rather by identifying certain
statistical regularities that can be utilised for building
effective selling strategies (Agrawal et al., 1993). A user
model in this case can contain a filtered set of transactions
matched against an association rule of items bought together
or satisfying some linear pattern of buyers’ behaviour,
or belonging to a cluster of similar buyers. Recently this
approach has been employed for modelling web users as
well (Baumgarten et al., 2000).
2.3 User model elicitation
There are two principle ways for an adaptive system to
obtain user modelling information: to ask the user directly
or to derive it based on the user’s activity with the system.
Many systems use a combination of these approaches, as the
natural flow of the ‘user-system’ communication (defined
by the task the user tries to complete) often requires active
user feedback. For example, a student, solving problems in
an ITS, has to provide intermediate and final answers, based
on which the system can infer their knowledge of target
concepts. Other systems, however, rely only on their ability
to mine information about the users from the logs of their
actions by applying machine learning techniques. If the
main task of a user does not imply any direct input to the
system, such an approach is preferable, as it does not
intervene with the task-related activity and does not increase
the user’s cognitive load. Finally, some adaptive systems
actively involve the user in the modelling process.
They may allow or even encourage users to directly modify
their user models. This section describes briefly these
elicitation technologies.
2.3.1 User model inference based on rich feedback
The most typical examples of adaptive systems receiving
rich feedback from users come from the broad class of
NLP systems. In intelligent dialog systems user modelling
has been summoned to help in recognising the
goals/plans/beliefs of the user and to provide a basis for a
more justified and robust dialog actions. Typically,
the interface of a dialog system is text-based, where a user is
able to enter his utterances in free manner, either asking
the system a question, or giving an answer to the system’s
qualifying question. Both users’ answers and questions are
used to gradually construct the user model; the system’s
responses are often driven by the ‘intention’ to refine some
of the modelling assumptions. For example, in Grundy
(Rich, 1979), the system plays the role of a librarian helping
the user to choose an appropriate book.
To elicit individual user models, intelligent dialog
systems of the early user modelling age usually followed
one of two ways: they either attempted to ask the user a
fishing question, or utilised rules, predicate logic and other
classic AI reasoning techniques, especially, such used for
plan recognition. Many of the developed reasoning
components and ad-hoc heuristics were domain-dependant
and could not be generalised for other tasks. The most
detailed overview of the early works in this area is provided
by Wahlster and Kobsa (1989). Zukerman and Litman
(2001) analysed some of the more recent efforts to employ
user modelling for NLP applications.
The second large class of adaptive systems relying on
both the rich user feedback and their inference capabilities
is constituted by various AESs and especially by ITSs.
The developers of ITS extensively use technologies for
reasoning under uncertainty. A typical user input in an ITS
is providing an answer to a problem or taking a step towards
the problem solution. Based on this information, as well as
on the domain model and the problem description, the ITS
employs its reasoning mechanism to update the user model,
which will be later used in certain adaptive actions
(presenting a problem of optimal difficulty, generating
remedial hints, suggesting a review of a tutorial, etc).
Various approaches have been applied for deducing
user characteristics. The mechanism known as knowledge
tracing uses simple Bayesian inference to calculate the
probability that the user has mastered a certain skill based
on the prior probability for that skill and the evidence
provided by the user (a correct/incorrect solution of the
corresponding problem) (Corbett and Anderson, 1994;
Galeev et al., 1994). A generalisation of this methodology is
employed by several systems using Bayesian Networks
(e.g., Jameson, 1992). The Bayesian Network in Epi-Umod
(de Rosis et al., 1992) models user knowledge as an
overlay over the network of domain concepts. Conditional
probabilities represent the mutual effects of knowledge
of relevant concepts; hence, the propagation of user
knowledge over this network takes into account the
prerequisite-outcome relationships between them.
In addition to Bayesian inference, some other
technologies have been also used for this purpose, such as
the Dempster-Shafer’s approach (Petrushin and Sinitsa,
1993), Hidden Markov models (Beal et al., 2007), or Fuzzy
Logic (Capuano et al., 2000).
Ontological technologies for user modelling
2.3.2 Unobtrusive user modelling
In order to support users in the most efficient way and
reduce their cognitive load, an adaptive system should try to
minimise the interference with the main task performed
by the user needed to maintain the adaptation. The
exponential growth of information and users on the web,
as well as the transfer of many services and activities
to electronic and online forms, call for applications that
support users unobtrusively by navigating them to the
desired information or filter out the non-relevant web pages,
documents, products, etc.
Webb et al. (2001) specify the challenges of unobtrusive
user modelling based on machine learning techniques.
The amount of information collected by an adaptive
application about their users is often not enough to build a
straightforward computational model of acceptable accuracy
(small datasets). Generally, user modelling is a dynamic
task; hence the parameters characterising a user are likely to
change over time, however, ML modelling algorithms are
often not able to adjust to these changes quickly enough
(concept drift). User modelling applications are supposed to
operate in an interactive mode, however, many of the ML
algorithms take a long time to converge (interactivity vs.
computational complexity). Supervised ML algorithms
require explicit labels. In the context of user modelling,
such labels often can be provided only by the users
(for example, the user notifies the system that she or he does
or does not like this webpage). Unfortunately, users are
known for being unwilling to provide information, which is
not directly connected to their needs (lack of labelled data).
Pierrakos et al. (2003) and Mobasher (2007) provide
detailed description of the main stages of information flow
in these applications as well as the techniques they use on
every stage. There are three main stages common for all
data mining systems: data collection, data pre-processing
and pattern discovery. Some applications perform the last
two stages in offline mode.
On the first stage, data from different sources is
collected, combined and structured to provide the basis for
the following processing. The main source of data is
provided by the web server’s transactional logs, which store
the basic usage data, the click stream of users accessing
system’s resources (date/time, requesting IP address,
requested resource, HTTP method, user platform
characteristics, etc.).
Data pre-processing involves several tasks. First, the
raw click stream data should be cleaned from the references
to non-informational resources (graphics, audio, style
sheets, etc.) and transactions generated by spiders.
Then the missing (due to the caching) transactions are
generated based on the analysis of the site hyper-structure.
Other important tasks performed on this stage include
user identification and session separation. This step is very
important, since every session is characterised by a specific
information task and represents a logically complete pattern
of user’s navigational behaviour.
The final stage, which is most related to user modelling,
is pattern discovery. On this stage, the application mines
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from the processed and enriched transactional data
statistical patterns representing observed usage regularities,
which are later utilised for recommending an appropriate
resource, navigating towards a relevant web page or
retrieving a document with desired features. There are
several groups of mining techniques coming from the field
of ML: clustering, classification, association rules,
sequential patterns, and latent variable models.
Clustering methods have been used mainly for two
purposes. User sessions have been clustered to mine the
typical navigation patterns. As a result, the systems were
able to recommend hyperlinks based on the other sessions
in the cluster (Yan et al., 1996), to generated an index page
navigating the user to all pages from the current session’s
cluster (Perkowitz and Etzioni, 2000), etc. Collaborative
filtering systems often use offline clustering of users and/or
items to remedy the scalability problem (Mobasher, 2007).
Classification has been less popular than clustering
among the adaptive data mining applications, because of
the need of pre-classified (labelled) data, which is rarely
available. Most often, classification has been used for
building descriptive models of user interests. For example,
when a user browsing on the web chooses to save, print or
bookmark a certain page, a classification component can
take it as a good indicator of the user’s interest in the
content of this web page. The pioneer Letizia system uses
this approach to recommend links potentially interesting
to the user (Lieberman, 1995).
Association rules and sequential patterns are very
similar and both have been developed as techniques for
market basket analysis. The difference between them is
based on whether the order of transactions is taken into
account by the algorithm or not. While sequential patterns
utilise this information, association rules containing the
same items but in different order are considered identical.
Similar to clustering, these techniques are used to predict
the possible navigational path of the user and support him
by recommending a proper shortcut (Mobasher et al., 1999).
Several latent variable models have been employed
by the adaptive data mining applications (see, Mobasher,
2007) for a comprehensive review). Their common premise
is to model some hidden (unobservable) factors influencing
observable user behaviour by introducing a latent variable
(or a set of variables). Latent variable models successfully
apply probabilistic inference to uncover semantic
relationships hidden in the data. For example, Jin et al.
(2005) used probabilistic latent semantic analysis to predict
information tasks of a user with a set of latent parameters
based solely on the navigational patterns. Pierrakos and
Paliouras (2005) exploited similar technique to uncover
hidden motives of web users and on that basis to model
online user communities.
2.3.3 Open, editable and interactive user modelling
In 1988, while discussing problems of intractability of
student modelling, Self (1988) suggested an idea opposite
to the traditional user modelling approaches described in the
previous sections: “Avoid guessing – get the student to tell
you what you need to know”. He suggested to
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“make the contents of the student model
open to the student, in order to provoke the
student to reflect upon its contents and to
remove all pretence that the ITS has a perfect
understanding of the student …” (Self, 1988)
Since then, a number of adaptive systems have explored the
effects of opening the user model and providing the user
with different levels of control over it (inspecting,
interacting, scrutinising, modifying). Most of these systems
are AESs, so, it is more common to talk about open learner
modelling (Kay, 2001).
The first implementation of this idea was the tool named
skillometer (Corbett and Anderson, 1992) (see Figure 1).
It contains a list of skills that a student is to achieve with the
system’s help and the current state of proficiency for each
skill, modelled by the system. Skillometers provide an
example of both: the simplest visualisation of the user
model and the most basic mechanism of ‘user-model’
interaction (users are informed about their current learning
state). More complex interfaces have been developed to
advance the idea of open user modelling in both directions.
For example, Zapata-Rivera and Greer (2004) presents
ViSMod, a system supporting visualisation of Bayesian
learner models (see Figure 2). The visualisation presents
nodes and relations between them, and prior and conditional
probabilities; it allows the user to set up evidences and
explore the model in ‘what-if’ scenarios.
Figure 1
Skillometer – the basic open learner model
Source: Corbett and Bhatnagar (1997)
Flexi-OLM explores further several elaborate techniques
for presenting learner models:
“hierarchy, a logical grouping of related
concepts; lectures, where topics are organised
the same as in the related lecture course;
a concept map showing relationships between
the topics; prerequisites, showing possible
sequences for studying topics; index, an
alphabetical list; ranked, where topics are
listed in order of proficiency; and a textual
summary.” (Mabbott and Bull, 2004)
It also provides students with multiple functionalities for
communicating with the model. A student can browse
the model and reflect upon its content; she or he can directly
edit the model, by providing the self-estimation of the
knowledge level for a particular concept; if not certain about
his or her knowledge a student can ask the system about the
reason for their grade and try to persuade it by answering a
series of test questions (Mabbott and Bull, 2006).
Figure 2
Open Bayesian learner model (see online version
for colours)
Source: Zapata-Rivera and Greer (2004)
Another example of an open learner model requiring active
participation of the learner in the modelling process
is provided by Dimitrova (2003). The STyLE-OLM system
implements a platform, where the conceptual model of
learner knowledge is built interactively, through negotiation
between the learner and the system.
Scrutable user modelling provides a user with an access
to the internal system’s modelling assumptions (Kay, 1999,
2006). A user not only can inspect the current state of the
system’s beliefs about her or him, but is also capable to
‘scrutinise’ the model and get an answer to the questions
like: “What does it mean?”, “How well do I know this
topic?”, “Why my knowledge level is X?”, etc.
Kay (1999) demonstrates the usefulness of scrutable
user modelling in a text editor help system and adaptive
movie recommender. Ahn et al. (2007) presents the
YourNews system, investigating the feasibility of open and
editable keyword-based user profiles for adaptive news
recommendation.
Figure 3 shows the interface of YourNews; users can
observe their profiles, built by the system, remove some
of the keywords from the profile (crossed words), or add
a new keyword to improve the recommendation.
Figure 3
YourNews: ediatble user profile for adaptive news
reccomendation (see online version for colours)
Source: Ahn et al. (2007)
Ontological technologies for user modelling
The preliminary results demonstrate that unlike the
concept-based editable user models, keyword-based models
are not edited by users to their advantage. This fact can be
explained by the shallow nature of keyword-based user
profiles. A concept-based domain model consists of
elements providing users with comprehensible semantic
information; therefore concepts can successfully serve as a
basis for the open and editable user modelling. On the
contrary, the meaning of automatically populated keyword
vectors is based on statistical regularities found in the
content. A single keyword does not represent a solid peace
of information by itself; therefore keywords cannot ensure
consistent editable user modelling. A blending of these
approaches is a perspective direction of research. A system
capable of translation between keyword-based and
concept-based models would benefit of both: the automatic
modelling of open-corpus data and semantically rich user
profiles.
3
Ontologies
Since Tim Burners-Lee put online his SW Road map
(Berners-Lee, 1998) and Scientific American published the
seminal SW paper (Berners-Lee et al., 2001), the main idea
of SW has not changed much. The SW is seen as a web of
meaningfully described information, available for automatic
discovery and integration across distributed applications.
However, it has been recognised by now that the time
needed for the realisation of this idea will be much longer
(Shadbolt et al., 2006). The amount of available RDF-based
metadata stays very small compared to the volume of the
existing information on the web. The broad reuse and
sharing of web data on the basis of robust and commonlyaccepted ontologies, as well as the large-scale agent-based
mediation of web services, are not happening yet.
Nevertheless, there are local stories of success. Some
companies are already developing SW applications
(Radar_Networks, 2008; Reuters, 2008; SemantiNet, 2008;
Zemanta, 2008; Benjamins, 2006; iSOCO, 1999). The
number of researchers working on SW problems is
constantly growing. Several closed communities have
become enough motivated and well organised to create
small ‘islands’ of SW (Health Care and Life Sciences
(W3C, 2007), Public Administration (SWED, 2004),
Engineering (SWOP, 2005), etc.). Overall, the SW initiative
has resulted in the development of a number of useful
technologies. Many of them are related to different aspects
of KR employing web ontologies. Although ontologies are
not a necessary element for any SW application, if the task
of the application goes beyond the integration of simple web
data and requires the representation of knowledge on the
web, ontologies provide the mechanism to do so.
3.1 Ontology representation and development
3.1.1 RDF and RDF-schema
Resource Description Framework (RDF) (Lassila and
Swick, 1999) has been proposed by W3C as the major
39
mechanism for metadata representation on the web.
The RDF model is similar to the Semantic Network
formalism and could be visualised as a directed graph.
The elementary particle of the RDF model is a resource
representing anything that can be referred by URI.
To uncover a resource’s semantics, one should link it to
other resources. RDF statements provide the mechanism for
organising resources into triples “subject-predicate-object”.
Every triple represents a directed semantic link from one
resource to another. As a result, interlinked resources
organise a semantic network.
The original RDF vocabulary is limited to the basic set
of primitives for managing descriptive metadata. To create
richer RDF documents, one needs more advanced tools
for developing collections of RDF terms enriched with
interpretable semantics. For these purposes W3C has
proposed the RDF vocabulary description language – RDF
Schema (RDFS) (Brickley and Guha, 1999, 2004). RDFS
provides two important extensions for the RDF model. First,
it specifies the mechanism for defining classes of resources
and developing hierarchies of classes and properties.
Second, it allows the restriction of property applicability to
the resources from a particular class. In contrast to pure
RDF, which can be used solely for descriptive metadata
specification (e.g., “who is the author of the resource?”)
RDFS allows the design of lightweight domain ontologies
and specification of resource semantics (“what is the
resource about?”) in terms of such ontologies.
3.1.2 The web ontology language
Although RDFS can be used as a representation formalism
for web ontologies, its expressiveness does not allow the
creation of elaborate domain models beyond simple
class hierarchies. If the task requires the creation of a
heavy-weight ontology modelling deep-level domain
semantics, a more complex ontology language should be
used. Well-defined semantics is necessary for supporting
machine interpretability, as it allows avoiding potential
ambiguities in the description interpretations. Hence,
the higher the formality of the ontology language, the more
complex reasoning it allows and the more useful the
developed ontologies can be.
In 2004 W3C recommended the Web Ontology
Language (OWL) as the major formalism for ontology
representation on the SW (McGuinness and van Harmelen,
2004). OWL was developed as a revision of the
DAML + OIL ontology language (Connolly et al., 2001)
and inherited most of its features. The consistency of OWL
with the lower-level web standards has been preserved.
OWL is based on the generic RDF model; it uses
RDF/XML syntax and the RDFS terms are naturally
integrated in the OWL vocabulary. At the same time, OWL
introduces a number of new features to handle semantically
complete metadata models. The creators of OWL built
the language upon such rich representational formalisms as
frame theory (Minsky, 1974) and description logics (Baader
et al., 2003) (for a detailed analysis of the origins of
OWL semantics (see Horrocks et al., 2003). As a result,
40
S. Sosnovsky and D. Dicheva
OWL allows modelling of all the major components of
formal ontologies: classes (or concepts), individuals
(or instances), properties (including class relations and
instance attributes), and axioms (or constraints).
There are three versions of OWL and each provides
ontology developers with more expressiveness than the
previous one: OWL-Lite, having a very limited set of
features beyond basic class hierarchies; OWL-DL,
enhancing OWL-Lite with the functionality of description
logic; and OWL-Full, combining the expressiveness
of the OWL-DL vocabulary and the freedom of the RDF
model.
3.1.3 Other semantic web representation
technologies
Although the basic metadata representation technologies
have been introduced a while ago, there still exist a number
of well-recognised metadata problems slowing down the
emerging of the SW. As a potential answer for these
problems, several new RDF-based technologies are
currently undergoing standardisation in W3C.
One of the biggest problems of the SW is the simple
lack of metadata-enriched web content. For spreading the
semantic technologies some critical mass of RDF-annotated
content has to be accumulated. The true power of the web
was revealed only when enough information appeared in
HTML format and the separate HTML pages became
connected to each other. The same effect should eventually
bring SW applications from the research labs to wide use
by the general public. However, currently the volume of SW
metadata is very far from web-like ubiquity. According to
Pandia (2007) the size of the web in February 2007 was
somewhat between 15 and 30 billion of documents. Swoogle
(Semantic Web Search Engine) (Ding et al., 2004) can
recognise currently 2.2 million RDF documents (Swoogle,
2007), which is only about 0.01% of the overall web
content.
To remedy the insufficiency of RDF-based metadata on
the web, W3C proposes several solutions. One of them is
provided by Gleaning Resource Descriptions from Dialects
of Languages (GRDDL) (Connolly, 2007). GRDDL is a
new technology specifying a formal way to retrieve RDF
content from XHTML pages and multiple XML-based
microformats existing on the web. XML Transformation
languages such as XSLT can be used to translate between
different XML dialects like XHTML and RDF/XML.
Another technology trying to address the same problem but
from a different angle is RDFa (that, arguably, stays for
RDF-Attributes) (Adida and Birbeck, 2007). RDFa follows
the examples of such technologies as SHOE (Luke et al.,
1996) and Ontobroker (Decker et al., 1998). Instead of
converting the existing HTML pages into RDF, it provides a
mark-up extension for incorporating RDF-based metadata
attributes into HTML pages.
The lack of SW metadata is partly related to the lack
of well-accepted RDF-based vocabularies. There are
successful examples of widely-used collections of
RDF-terms, such as Dublin Core for digital libraries
(DCMI, 1999), and FOAF for social networks (Brickley and
Miller, 2005), however they cannot support the metadata
creation for every application task. Simple Knowledge
Organisation System (SKOS) is aimed to provide a general
model and a core vocabulary for representing such
knowledge structures as thesauri, glossaries, taxonomies,
folksonomies, etc. The main objective of SKOS is to
support easy development and publication of controlled
vocabularies for the SW (Miles and Brickley, 2007).
3.1.4 Ontology development tools
The availability of well-accepted standard technologies for
ontology representation, based on formal models (RDF
graph model, Description Logic model) and machinereadable syntax (XML), greatly facilitates ontology
development. Before the introduction of the RDF-based
representational models, no common way for sharing
developed ontologies existed; the ontology authoring tools
used their own formats, e.g., Ontolingua (Gruber, 1993)
used KIF and OKBC, Ontobroker (Decker et al., 1998) used
F-Logic. For some previous XML-based ontology
languages like XOL (Karp et al., 1999) and OML (Karp
et al., 1999), it was possible to use general XML editors.
The RDF-based ontology languages allow an ontology
author working with one development tool to open, explore
and reuse ontologies developed by other authors in other
tools and to share their ontologies.
Multiple tools for ontology development have been
recently created and multiple reviewing papers comparing
different aspects of these systems have been published
(e.g., Gomez-Perez et al., 2002; Polikoff, 2003; Youn and
McLeod, 2006) provide an aggregated review of some two
dozens ontology development tools).
The most famous and popular line of tools for ontology
development is the Protégé product family, developed at the
Stanford University School of Medicine (Noy et al., 2001).
Protégé has two versions: Protégé-OWL, for creating SW
ontologies, and Protégé-Frames, for developing knowledge
models compatible with the OKBC framework. The
standard functionality of Protégé contains an extensive set
of features to work with basic elements of OWL (classes,
properties, individuals and constraints), however, the real
power of Protégé comes from its extensible architecture.
Protégé API allows a third-party’s plug-ins to be easily
integrated into the system and extend it with advanced
functionality for visualisation, import/export, reasoning,
querying, mapping, and integration with other tools
(the number of registered plug-ins at the moment of writing
this manuscript is 87). Another important feature of Protégé,
distinguishing it from most of the other ontology
development tools, is scalability.
3.2 Ontology mapping
Ontologies are intended to represent consensual knowledge
in the domain of discourse. Ideally, a domain ontology
should be discussed and verified by all interested
comminutes. Unfortunately, achieving such a level of
Ontological technologies for user modelling
41
agreement is unrealistic in the web settings. Most often,
related communities differ in their perspectives on the
same domain in such a degree that common ontological
commitment across all interested parties is impossible.
However, from a practical point of view, this should not be
even required, as an ontology representing a community’s
perspective provides a unified-enough model that can
effectively serve as a basis for semantics sharing and data
integration.
As a result, there are many ontologies representing the
same domain and their number will continue to grow as
the SW ideas disseminate further. Even larger is the number
of overlapping ontologies (ontologies modelling the same
concept(s)). For example, the Swoogle search engine
(Ding et al., 2004) currently retrieves 3900 RDF documents
referring to the resource named ‘user’.1 Some of these
documents are actual ontologies defining their own versions
of the concept ‘user’ reflecting different viewpoints and
notions but still modelling the same (or very close) entities
in the world. This number becomes even greater if we
consider ontologies defining the same or very close concept
using different labels (e.g., ‘actor’ or ‘human agent’).
To find the correspondence between identical and
relevant concepts defined in different ontologies, various
ontology integration techniques have been developed over
the last ten years. These techniques have formed, arguably,
the most dynamic area of ontological research named
‘ontology mapping’.2 Dozens of theoretical frameworks,
formal algorithms and actual systems for ontology mapping
have been created and several comprehensive reviews have
been written (see for example, Kalfoglou and Schorelmmer,
2003; Noy, 2004).
3.2.2 Ontology mapping based on lexical
information
3.2.1 Ontology mapping based on a reference
ontology
An ontology represents the objective relationships among
the knowledge items of the domain. Therefore, if two
ontologies of the same domain are designed in a meaningful
way, the parts modelling the same piece of domain
knowledge should have similar structures in the ontology
graphs. Thus, another source of evidence that can signify
the alignment among elements of two ontologies is the
resembling structural patterns in the ontology graphs.
Most of the ontology mapping techniques rely on
structural relationships among ontology elements to a
certain degree. The simplest reasoning pattern would be
similar to the following: if two concepts of the ontologies
are mapped, their super-classes are likely to map, same as
their subclasses, same as their siblings. Similar heuristics
are implemented in the PROMPT ontology mapping
algorithm (Noy and Musen, 2003). Once it finds a
confirmed mapping, it starts harvesting the neighbourhoods
of the mapped elements to suggest new mappings.
PROMPT’s later modification AnchorPROMPT analyses
ontologies as directed labelled graphs, where nodes
represent classes and links – predicates. AnchorPROMPT
considers two sub-graphs from the ontologies and compares
possible paths through the subgroups restricted by anchors.
In a limited number of cases, the ontologies being mapped
can contain references to a common ontology. Most often,
the common ontology models very general notions of
nature, such as time, space, process etc. Several initiatives
to create general ontologies exist: Suggested Upper Merged
Ontology (SUMO) (Niles and Pease, 2001), Descriptive
Ontology for Linguistic and Cognitive Engineering
(DOLCE) (Gangemi et al., 2002), CYC (Lenat, 1995),
WordNet (Miller et al., 1990) etc.
The natural purpose of a general ontology is to be
extended by domain-specific ontologies. If concepts of two
ontologies are connected to concepts of an upper-level
ontology, such linkage serves as a valuable source for
identification of correspondences between the mapped
ontologies. For example, if two ontologies have their
concepts ‘actor’ and ‘human agent’ linked with ‘sameAs’
relations to the single concept ‘user’ of the upper ontology,
this provides strong evidence that these two concepts from
different ontologies actually model the same entity in the
world.
Probably the most valuable information for ontology
mapping comes from the lexical labels given to the elements
of the ontologies by their developers. Nicely designed
ontologies also contain natural language comments and
definitions explaining the meaning of the classes, predicates
and axioms constituting the ontology. Ontology developers
tend to maintain this lexical information meaningful and
consistent across the ontology, therefore lexical similarity
of two elements often indicates the existence of semantic
similarity.
Utilisation of lexical information is traditional for
ontology mapping techniques. Even if the system applies
other mapping methods, it somehow exploits the lexical
information as well. For example, Hovy (1998) describes
a method for semi-automatic alignment of domain
ontologies to a central ontology. The method is based on a
set of heuristics for lexical comparison of concept labels
and definitions across the ontologies. The lexical component
of the method first breaks composite names into words,
then analyses word sets for common substrings and
computes the ratio of common words. Finally, a limited
analysis of taxonomical relations among the concepts is
performed. The GLUE algorithm (Doan et al., 2002)
combines the results of two separate mappers, one of which
is based on the lexical similarity of extended class names
(where an extended class name is a concatenation of a class
name and names of all its super-classes up to the root class
of the ontology).
3.2.3 Ontology mapping based on ontology structure
42
S. Sosnovsky and D. Dicheva
If, as a result of the analysis, two classes often occur in the
same parts of the graphs, they represent similar domain
concepts.
3.2.4 Ontology mapping based on instance corpora
Some ontology mapping algorithms try to utilise not
only the content of ontologies themselves, but also the
corpora of textual instances annotated with the ontology
elements. An access to sets of instances significantly
increases the amount of data available for the ontology
mapping algorithm. To manage such volumes of
information effectively, ontology-mapping algorithms
implement various probabilistic and machine learning
methods. This allows them to benefit of well-developed
methodology for statistical reasoning based on unstructured
textual data.
One example of such algorithms is implemented in the
project FCA-Merge (Stumme and Madche, 2001). It applies
Formal Concept Analysis for bottom-up ontology merging.
The combined set of class instances of the original
ontologies undergoes lexical preprocessing and then is used
to create the merged concept lattice of the two ontologies.
After the resulting lattice is automatically pruned, a human
expert should analyse it and manually extract from it the
resulting ontology.
The GLUE algorithm (Doan et al., 2002) is another
example of machine learning-based ontology mapping.
Its instance-based mapping component takes as input two
ontologies along with the corresponding sets of instances.
On the first step, for every pair of concepts from the two
ontologies GLUE estimates the joint probability of their
distribution in the joint set of instances. For this purpose,
GLUE trains a classifier for every concept using its instance
set as positive (instance is annotated with the concept) and
negative (instance is not annotated with the concept) cases
and then cross-classifies instances of the second ontology
using the just-trained classifiers. Then GLUE repeats the
procedure with the ontology roles reversed. Thus, for every
pair of concepts GLUE is able to break the joint set of
instances into four subsets (instance is classified positively
for both concepts, for one of them, or for none them). The
obtained joint probability distributions are converted into
concept similarity estimations. As a final step, for every
concept GLUE chooses the mapping candidate from the
other ontology, for which it has the maximum similarity
value.
3.3 Ontology visualisation
Most of the AI and SW research refers to ontologies as the
means for conveying semantics to machines and helping
machines to understand each other. However, the potential
use of ontology in a knowledge-based application is not
reduced to interfacing programs; a user interface of a system
can also exploit ontologies for effective navigation of a user
through the content (Fluit et al., 2005; Guarino, 1998).
Such an interface must present to the users the elements of
the underlying ontology in a meaningful and useful way.
The difference of ontology visualisation from the
general visualisation of a structured unit of information
spreads from the exceptionally rich semantics of ontologies.
Ontology visualisers should provide extra capabilities for
explicating this semantics (Alani, 2003). Effective ontology
visualisation could be helpful in a number of scenarios,
which can be reduced to two general tasks:
•
visualisation of the ontology itself for the purposes of
ontological analysis and understanding of the domain
semantics
•
ontology-based information visualisation for effective
navigation (e.g., ontology-based search engine could
present the results of a user’s query not as a flat list of
hits, but as an ontology-based structure reflecting the
semantics of the hits (Fluit et al., 2005).
3.3.1 Visualisation of ontologies
Every ontology editor has some ontology visualisation
capabilities to assists ontology designers in their job (e.g.,
to observe the results of current changes, validate design
decisions and check for inconsistencies and mistakes).
Typically, an ontology editor presents an ontology as a class
hierarchy. Class hierarchies provide ontology developers
with simple navigational capabilities. They facilitate the
definition of class-subclass relations and help to focus on a
certain class for defining its predicates and instances.
However, supporting ontology developers is not the
only goal of ontology visualisation. A nicely visualised
ontology could be used as a powerful cognitive tool.
The ontology itself provides valuable information
describing domain semantics in a structured and unbiased
way. Comprehendible visualisation of an ontology can help
the user to understand and analyse information on the
conceptual level.
To meet these requirements some of the ontological
tools support more advanced visualisation layouts. The best
example here is Protégé, whose plug-in architecture
accommodates a dozen of visualisation tools, including
(the two most popular) TGVizTab (Alani, 2003) and
Jambalaya (Lintern and Storey, 2005; Storey et al., 2001).
3.3.2 Ontology-based information visualisation
Several recent projects apply visualisation techniques for
implementing ontology-driven access to large amounts of
information. Although ontology visualisers mentioned
above can also be used for providing access to class
instances (that can carry valuable information), there is a
major difference between them and the projects described
here. The previous section focused on the visualisation of an
ontology itself that supports a user in conceptual knowledge
analysis and comprehension of domain semantics. This
section concentrates on designing interfaces for meaningful
Ontological technologies for user modelling
information access based on the domain semantics that
facilitates such activities as efficient information seeking
and navigation.
An example of a project designed to support users’
exploratory activities is Spectacle (Fluit et al., 2005).
It provides a convenient interface for navigating through a
corpus of structured information and finding interesting
informational resources. Spectacle introduces a novel
visualisation mechanism called Cluster Maps. Cluster Maps
are designed for presenting information annotated in
terms of lightweight ontologies as networks of clusters
(see Figure 4). Every cluster represents a set of information
resources attached to a concept annotating these resources.
If a set of resources belongs to a number of concepts,
they form a cluster attached to all the concepts involved.
Every small sphere in a cluster’s bubble represents an actual
single resource. The concepts themselves are connected
with directed links denoting a hierarchical relation. A click
on a concept node makes this node a focal concept; it opens
the neighbouring concepts along with their resource cluster
and converge the irrelevant concepts that stay too far from
the focal node in the hierarchy. If the corpus of resources is
annotated using different categories, a user can browse
the same information from different perspectives.
Figure 4
Visualisation of job vacancies organised by economy
sector in Spectacle (see online version for colours)
43
•
manual authoring of metadata is time-consuming and
expensive
•
manual authoring of metadata is difficult and error
prone
•
manually created metadata is often subjective (domain
experts developing a metadata vocabulary for the same
field or annotating the same documents are likely to
disagree on a large portion of the work)
•
metadata creation is never ending (new documents
appear, new versions of ontologies are developed,
new fields and tasks arise)
•
metadata storage and ownership problem (where the
manually created metadata should be stored, who is
responsible for its support, who owns it?)
•
metadata created manually by the owner of a document
may be not trustworthy (he can be incompetent or
malicious, e.g., for spam reasons).
These issues highlight the importance of developing
online metadata services (similar to the modern web
search engines) relying on technologies for automatic
semantic metadata creation. Two research subfields try
to employ ontologies in this process. Ontology-based
annotation combines a set of techniques for automatic and
semi-automatic description of web documents with semantic
metadata. Ontology learning studies methods for building
ontologies from scratch or enriching existing ontologies.
3.4.1 Ontology-based annotation
Annotation of documents with semantic metadata can be
used for different purposes:
Source: Fluit et al. (2005)
3.4 Ontology-based knowledge acquisition
The knowledge acquisition bottleneck remains the main
problem for the SW technologies dissemination. Although
new technologies for developing metadata vocabularies
(e.g., SKOS) and simplified embedding of RDF into HTML
content (e.g., RDFa) have been introduced, the large-scale
manual development of semantic metadata stays rather an
unrealistic scenario due to a number of reasons:
•
to support integration of disperse information in the
document space (knowledge-based implicit linking
of documents vs. traditional explicit linking)
•
to ensure better indexing and retrieval of documents
(based on the document semantics vs. shallow
keyword-based content modelling)
•
to support content-based adaptation (by modelling
document content in terms of the domain model).
Ontology-based annotation can be defined as a process of
creating a mark-up of web resources using a
pre-existing ontology and/or populating knowledge bases
via the mediation of marked up documents (Gomez-Perez
et al., 2002). Figure 5 visualises the typical process of
ontology-based text annotation. Some of the terms in the
text are matched against the elements of the ontology. As a
result, the knowledge-based system becomes aware, that
this extract speaks about an entity XYZ and entity Bulgaria.
The ontology reveals also that Bulgaria is a country, while
XYZ is a company located in London, UK, where London is
a city, and UK is a country, different from Bulgaria.
44
Figure 5
S. Sosnovsky and D. Dicheva
Ontology-based annotation (see online version
for colours)
Source: Popov et al. (2004)
Modern ontology-based annotation systems provide
manual, semi-automatic or fully automatic annotation. The
ontologies used for annotation can be represented in
different languages. Some systems annotate web resources
only in terms of a specific ontology, while others allow
loading external ontologies. The resulting annotation can be
represented in RDF, XML or other formats; it can be
attached to the annotated documents or stored separately on
a dedicated server. Some systems use for annotation only
class names, while others allow annotating with relations,
instances and even entire triples. The architecture of a tool
can vary from an API to a client-server application. Finally,
the scale of annotation is a very important issue; while some
tools aim at annotating relatively small sets of documents,
others provide large-scale annotation of millions of
resources.
Semi-automatic ontology-based annotation
Semi-automatic (or user-centred) ontology-based annotation
is the current mainstream approach for semantic markup
of web documents. Its main idea is based on the traditional
machine learning methods for text classification.
While a human annotator creates a markup for initial
limited set of documents, the information extraction
component (such as Amilcare (Ciravegna, 2001)) learns the
associations between content features and ontology
elements. When ready, the algorithm continues the
annotation in automatic fashion. At the end, the human
annotator usually has an option to review the results of the
automatic phase and make necessary corrections.
The tool functionality and user interface vary from one
system to another. Some systems provide extra features.
For example, MnM can work with several information
extraction components performing the automatic phase of
annotation (Vargas-Vera et al., 2002). It can access
ontologies stored on external ontology servers (such as
OKBC) through APIs and stores annotations on a dedicated
server. Another semi-automatic annotation system Melita
(Ciravegna et al., 2002) organises the annotation process
in an adaptive manner. It does not wait until the end of the
training phase, instead, it starts recommending annotation
candidates immediately after the human annotator has
provided some evidence.
S-CREAM (Handschuh and Staab, 2003; Handschuh
et al., 2002) can crawl previously annotated web resources
and use them as training data. It also supports annotation of
the so-called deep web (web content dynamically created
from relational databases thus not accessible for retrieval
and annotation by traditional tools) (Bergman, 2001). Some
experts estimate the proportion of the deep web with respect
to the shallow (static) web as 500 to 1. To annotate such
dynamic content, S-CREAM analyses the schemas of
underlying databases. It considers these schemas as
ontologies and applies ontology mapping algorithms to
identify the correspondence between the schemas and
the target ontology. The resulting mapping rules are used to
dynamically markup the generated documents.
Fully automatic ontology-based annotation
Unlike semi-automatic ontology-based annotation, fully
automatic approaches considerably differ one from another.
The algorithms employed are often based on ad-hoc
hypotheses rather than on a well-accepted methodology,
such as text classification. The common feature of these
approaches is the wide usage of NLP techniques. However,
the complexities of the employed NLP algorithms are very
different. Some systems, like COHSE (Bechhofer et al.,
2003), do not go beyond the identification of noun phrases
in the text and a trivial match of the found entities against
concepts from an ontology enriched with synonym
information from a thesaurus. Others, like AeroDAML
(Kogut and Holmes, 2001), in addition apply predefined
linguistic patterns to identify instances of typical categories
(date, time, money, etc.). Consequently, the first phase of
most algorithms for automatic annotation is usually the
same – the textual corpus is analysed for words or phrases
conveying the content semantics. The difference is in
choosing annotation candidates to map a piece of content to
the appropriate semantic tag from the ontology.
For example, the PANKOW algorithm (Cimiano et al.,
2004) attempts to rely in this process on the collective
intelligence harvested from Google users. After PANKOW
chooses candidate proper nouns that can be important for
the corpus, it builds a set of hypothesis queries from the
found candidate nouns and candidate annotating concepts
for these nouns. The queries are created using predefined
linguistic patterns. On the next step PANKOW tosses the
generated queries to the Google search engine (using
Google API). The results retrieved from Google are
compared based on the numbers of hits for every hypothesis
Ontological technologies for user modelling
45
query. The noun-concept pair, which formed the set of
queries with the highest cumulative hit rating is considered
the most related, hence that annotation is performed.
The algorithm of SemTag, a large-scale annotation
system (Dill et al., 2003), combines the results of lexical
analysis and the hierarchical relationships between the
concepts in the ontology. It uses the TAP taxonomy
(Guha and McCool, 2003), which contains more than
72,000 concepts, to annotate about 260 million web
documents. After document tokenisation, SemTag tries to
map tokens to the taxonomy concepts. Every token is
saved with a token signature – ten words before and
after it in the text. To distinguish between possible
candidate concepts for a certain token, a taxonomy-based
disambiguation algorithm is used.
Armadillo (Ciravegna et al., 2004) is another large-scale
annotation system. Unlike SemTag, it can use an imported
ontology as a source for annotation. The annotation process
is based on automatic pattern discovery and information
integration. It starts with a set of documents and a
predefined lexicon for annotation. Once Armadillo finds
a certain pattern (a match between a lexicon element and a
content fragment), it begins to look for its confirmation
beyond the initial set of documents. If a proper lexicon
extension is found, Armadillo starts looking for appropriate
patterns for it and then for information confirming these
patterns. The information from different sources is
integrated, the lexicon is expanded, and the found
annotations are stored in the form of RDF triples.
The process continues until the algorithm reaches a stable
point.
Gomez-Perez and Manzano-Macho (2005) provides an
extensive survey of ontology learning approaches relying on
mining semantic information from unstructured texts.
All such approaches are based on advanced NLP techniques
complemented with statistical and machine learning
algorithms. Many of them employ thesauri information,
most often provided by WordNet. Often, text-based
algorithms are used as components in a larger environment
combining outputs from several evidence providers to learn
a resulting ontology. For example, Text-To-Onto (Maedche
and Staab, 2001) integrates information from texts,
dictionaries, online databases and legacy ontologies.
Online dictionaries and encyclopedias, such as
Wikipedia, consist of well-organised corpora of thematic
pages interlinked together. A link between two pages
usually exists when there is a semantic link between the
corresponding concepts. The pages themselves have
consistent formatting, which facilitates concept extraction
and sometimes identification of hierarchical relations.
Several recent systems try to utilise such well-formatted and
well-written collections of documents for the purpose of
knowledge acquisition. For example, Ruiz-Casado et al.,
(2005) use patterns from Wikipedia pages to enrich
WordNet with new relations. TM4L, an environment for
creation and use of topic-focused, ontology-driven learning
repositories, supports the authors in building light-weight
domain ontologies by automatic extraction of concepts and
relationships from the Wikipedia and Wikibooks websites
(Dicheva and Dichev, 2007). TM4L uses Wikipedia as a
source for proposing concepts relevant to a particular
subject and as a source of ‘standard’ concept names.
3.4.2 Ontology learning
Elements being learned
Ontology learning aims at developing methods and tools
for automatic and semi-automatic acquisition of ontologies
from structured, semi-structured and unstructured sources.
This includes learning of new ontologies from scratch or
based on some preformatted sources, as well as enrichment
or adaptation of existing ontologies by extending or
modifying their structures. Shamsfard and Barforoush
(2003) identified several important dimensions for
comparing ontology-learning approaches: input data,
elements being learned, learning algorithm, and ontology
output.
Concepts are the basic ontology building blocks. Thus most
of the ontology learning systems start the learning process
with identification of domain concepts by extraction of
appropriate terms from the input corpus. Concepts can also
be created during the refinement phase from other concepts.
Consequently, two principal ways exist for concept learning
(Shamsfard and Barforoush, 2003): terminological concept
acquisition and semantic concept acquisition. The first
method includes linguistic analysis of the initial set of
documents, identification of key noun phrases, word sense
disambiguation, and synonym processing. The second
method is applied during the ontology refinement stage and
includes analysis of the ontology structure (relations
between concepts) and concept evaluation (according to
their attributes). As a result, some concepts can be merged
or broken into smaller concepts.
Generally, relations are harder to learn than concepts,
especially from unstructured sources. In such cases, the only
available information comes from the morphological
analysis of sentences containing identified concepts. If the
documents in the initial corpus are connected with
Input data
The staring points for different ontology learning algorithms
vary from unstructured corpus of plain-text documents to
data with well-defined internal semantic structure (such as
existing ontologies, database schemas, and lexical nets such
as WordNet). The majority of recent systems attempt to
exploit various semi-structured corpora of web resources
(HTML, XML and DTD documents, online dictionaries,
encyclopedias and thesauri, etc).
46
S. Sosnovsky and D. Dicheva
hyperlinks and the content of the documents has internal
structure (as in Wikipedia), these structural regularities
could be used to connect together concepts found in the
linked pieces of the corpus. Different approaches are used
for learning taxonomic relations, non-taxonomic conceptual
relations and concept attributes.
Axioms or rules are very hard to learn automatically.
Few systems are capable of extracting some. An example is
Hasti (Shamsfard and Barforoush, 2004), which can learn
a limited set of axioms in restricted situations.
Learning algorithm
There are currently no existing approaches for fully
automatic learning of heavyweight ontologies from
unstructured texts. The automatic algorithms are either
developed for enrichment of existing ontologies with new
concepts and/or relations (Faatz and Steinmetz, 2002) or for
learning light-weight ontologies from well-structured corpus
of documents (Apted and Kay, 2004). Most often, ontology
learning approaches require human intervention at least on
the refinement stage to validate the results of the automatic
learning process (Maedche and Staab, 2001).
The algorithms for ontology learning try to find
effective combination of techniques from other fields,
including computational linguistics, information retrieval,
machine learning, and database management. As a result,
the algorithms differ along many dimensions (Shamsfard
and Barforoush, 2003):
•
the degree of linguistic pre-processing (deep NL
understanding vs. shallow text processing)
•
the degree of automation (supervised/unsupervised/
cooperative)
•
the degree of interactivity (online/offline)
•
learning approach (statistical vs. symbolic, logical,
linguistic-based, pattern matching, template-driven
or hybrid methods)
•
learning task (classification, clustering, rule-learning,
concept formation, ontology population).
Systems supporting the full process of ontology learning
usually combine several algorithms to perform a number of
intermediate tasks and deal with multiple sources of
evidence.
Output ontology
Not all learning algorithms result in the creation of a
complete ontology. Some generate intermediate knowledge
structures and can be used as components in a bigger
ontology-learning environment. For example, the Cameleon
system (Aussenac-Gilles et al., 2000) finds the so-called
lexico-syntactic patterns, which are good indicators for the
presence of a conceptual relation; the final decision about
adding a new relation is made by a human expert. WOLFIE
(Thompson and Mooney, 1999) learns semantic lexicon
from NL texts; it handles polysemy and synonymy and
produces a set of terms and representations of their
meaning.
Text-To-Onto (Maedche and Staab, 2001), Hasti
(Shamsfard and Barforoush, 2004) and MECUREO (Apted
and Kay, 2004) are examples of systems learning complete
ontologies. However, the resulting ontologies differ very
much in their coverage, usage or purpose, content type,
structure and topology, and representation language.
4
Ontologies meet user modelling
The benefits of using ontologies for user modelling and
adaptation have been recognised by many researchers.
Mizoguchi and Bourdeau (2000), in their seminal work
listed ten problems of AI research in education and
proposed how more formal system design principles
relying on ontological engineering could help to overcome
those problems. Based on the analysis of the existing
research, the authors of Dicheva et al. (2005) developed an
ontology of ontological technologies for e-learning and
implemented a web portal driven by this ontology, which
facilitates the description and discovery of online resources
in this area (such as papers, workshops, research groups
etc). Winter et al. (2005) analysed the ways ontologies
can help student modelling. Among the advantages of
ontology-based student models they named
“formal semantics, easy reuse, easy portability,
availability of effective design tools, and
automatic serialisation into a format
compatible with popular logical inference
engines.”
A number of dedicated workshops have been organised by
several communities (for references see O4E, 2008) and
several pier-reviewed journals have published special
issues on application of ontological technologies for user
modelling and adaptation.
In this section, we present an analysis of this rapidly
growing field of research. When possible, we follow the
order and structure of the two previous sections.
Every subsection tries to demonstrate how a certain user
modelling technology benefits of employing particular
ontological technologies. Figure 6 shows the structure
of the section, the relations between its parts and
the corresponding technologies from the fields of user
modelling and ontologies. The relations signify only
the most important dependencies. (For “Ontology
representation technologies” that have influence on every
section the links are omitted.)
Ontological technologies for user modelling
Figure 6
47
Ontological technologies for user modelling (see online version for colours)
4.1 Ontologies for user model representation
Ontologies are in the first place a KR technology.
Thus, almost any user-adaptive system employing an
ontology for user modelling, on some level uses it for
representational purposes. Even when the major goal for
utilising ontologies is to the benefit of one of the derivative
ontological technologies (e.g., using ontology mapping
for user model mediation or applying ontology-based
learning for automatic construction of user model),
the primary design decision of the system developer would
be to represent some part of system’s knowledge as an
ontology. Therefore, several projects described in this
section will be mentioned again, when we talk about
ontologies for user model elicitation (Section 4.2) and
ontology-based user model interoperability (Section 4.3).
The diversity of ontological approaches for
representation of user models springs from both the vast
pool of research accumulated in the past on user model
representation and the opportunities, ontologies provide for
the new generation of adaptive systems. However, we can
identify two principle directions summarising the whole
spectrum of approaches. First, an adaptive system can
employ an ontology for modelling the structure of its
domain and then use elements of that ontology to represent
atomic user characteristics. The second direction is to
structure a complex user profile that models several
dimensions of user’s state as an ontology. The first approach
inherits its main ideas from the overlay user modelling
(Section 2.2.1), while the second one relates to the user
modelling dimensions (Section 2.1) and has roots in
stereotype user modelling (Section 2.2.3). A small set of
research concentrates on facilitating keyword-based user
modelling with the use of lexical ontologies such as
WordNet.
4.1.1 Ontology-based overlay user modelling
The most conventional implementation of ontology-based
user modelling is the overlay user modelling that relies on
a domain model represented as an ontology. A simplified
view on an ontology is a network of concepts. It provides a
natural basis for modelling a domain of discourse following
multiple examples in the area of AI and Intelligent Systems.
Over the years several network-based KR formalisms have
been explored, including Semantic Nets (Woods, 1975),
Concept Maps (Novak and Gowin, 1984) and Bayesian
Belief Networks (Pearl, 1988). While similar to the
48
S. Sosnovsky and D. Dicheva
mentioned modelling frameworks in the idea of
network-based domain representation, ontologies differ in
providing a basis for implementation of sharable and
reusable models with rich built-in inference capabilities.
Representation of a domain model as an ontology and
modelling a user’s knowledge, interests, needs, or goals as a
weighted overlay on the top of it enables the usage of
standard representation formats and publicly available
inference engines, as well as an access to a vast pool of
technologies for ontology mapping, querying, learning, etc.
Such straightforward overlay user modelling based on
ontologies is implemented, for example, in the misearch
system (Speretta and Gauch, 2005a, 2005b; Bouzeghoub
et al., 2003). misearch uses the Open Directory Project
(ODP) (Netscape, 1998) hierarchy as a reference model
to represent interests of a user querying information on the
web and to re-rank the search results according to the user’s
interest model.
Regional Browsing Agent (RBA) (Chaffee and Gauch,
2000) developed as part of the OBIWAN project (Gauch
et al., 2003) supports personalised web-browsing based on
users’ interest profiles. As an underlying ontology RBA
uses the Lycos hierarchy (Lycos, 1999), which contains
5863 concepts and has a tree depth of four. The concept
hierarchy is used to navigate the user to the most interesting
websites. The number of stars annotating an item in the
hierarchy visualises the system’s opinion about the user’s
interest in the corresponding category.
Employing principles of the formal ontological theory,
the domain ontologies can also benefit of such properties as
intentionality and explicitness, which allows building
unbiased and logically complete domain models (Guarino,
1998).
Another possible extension of the overlay user
modelling based on a domain ontology is to model user
characteristics by employing not only the concepts of the
ontology but also its relations and axioms. It could be
the knowledge of the fact that a human being ‘is-a’ mammal
(relation) or that every person “has two and only two”
biological parents (axiom); it can be an interest in anything,
which is a ‘part-of’ a particular laptop model, etc. We are
not aware of any system employing such modelling
techniques. A somewhat related idea is proposed in Sicilia
et al. (2004). The authors introduce a concept of learning
link for modelling relationships between learning objects
and propose an ontology of such links. Concepts of
this ontology are used to annotate the hyperlinks in a
hypermedia tutorial. The designed prototype system
adaptively navigates users taking into account not only the
concept knowledge but also the history a user has had
for the particular type of links.
The overlay user model can also consist of a subset of
concepts from the domain ontology. This is typical for
recommender systems representing user interests. The
presence/absence of a concept in the user model signifies
the presence/absence of interest in the corresponding
concept. The concepts can also have associated values
(weights, verbal labels), characterising the magnitude of
the interest. For example, the museum guide ec(h)o
represents user interests as a set of concepts from the
ontology of natural history (Hatala and Wakkary, 2005;
Hatala et al., 2005). The ontology is taken from the
corresponding part of the Dewey Decimal Classification
(Dewey, 1876). The strength of an interest is represented
by a weight calculated based on the user activity
(movements around the exhibition, stops in front of a certain
piece, playing a particular audio fragment). Based on the
model of interests, ec(h)o recommends to the user the next
best audio fragment.
A similar approach is implemented in MyPlanet
(Kalfoglou et al., 2001) – a recommender systems for
scientific information. The information is disseminated
in a form of e-Stories (news items). Users access e-Stories
in personalised manner based on their preferences.
The preferences are taken from a set of seven ontologies
representing research areas, research themes, organisations,
projects, technologies application domains, and people.
There is no weight associated with a preference; it is a
binary value. MyPlanet intensively uses ontological
inference to propagate user preference through an ontology
and across different ontologies. For example, if the user is
interested in a particular genetic algorithm (which is a
subclass of the Genetic Algorithm concept, which in turn is
a member of the Research Area category), and there
is an available news item on another genetic algorithm,
this story will be included in the system recommendation.
Stories describing Projects or Persons working on genetic
algorithms will be also retrieved.
4.1.2 Personal ontology views
Many projects using ontologies for modelling characteristics
of an individual user go beyond the classic overlay
approach. User models in such systems are not represented
as simple weighted masks over the domain model, but as
individual networks of concepts reflecting personal
conceptual structures in the domain of discourse. For
models of this kind, Huhns and Stephens (1999) introduced
the term ‘personal ontologies’ to designate a computation
model reflecting an individual perspective on the world.
The term has been criticised later in Kalfoglou et al. (2001)
for its contradiction to the definition of ontology as a model
representing a shared view on the domain. Accepting the
use of the concept itself, however, they suggested another
name – “personal ontology view”. The latter term conveys
the meaning of the concept more precisely, as the
ontology-based user model is not an ontology itself, but its
individualised permutation. We are going to follow
Kalfoglou et al. and call these models Personal Ontology
Views (POV). From a cognitive perspective, POVs are
consistent with the constructivist paradigm and cognitive
models like Concept Maps (Novak and Gowin, 1984),
as they represent domain conceptualisation from the
viewpoint of an individual. The POV implementations are
different in different systems. We distinguish two basic
variations: POV represented as a subnet of a domain
Ontological technologies for user modelling
ontology and POV as a unique conceptualisation, whose
structure can deviate from the underlying domain ontology.
The following two subsections analyse these variants in
detail.
POV as a sub-graph of the domain ontology
The implementation of a user model as a sub-graph of the
original domain ontology can be considered as an extended
version of the model containing only a subset of domain
concepts. In this case, not only concepts but also relations
and constraints can be included in the user model. If the
model contains only a subset of concepts, the inference
of possible semantic connections is somewhat limited, since
the actual inference happens in the domain ontology, when
the user model can provide only the activation nodes.
The sub-graph POV allows a more complete representation
of the user model. The inference can be implemented over
the POV itself that prunes the irrelevant parts of the domain
ontology and contains only the relevant ones.
This approach is realised in two adaptive recommender
systems: Quickstep and Foxtrot (Middleton et al., 2004).
Both systems recommend research publications. The
ontology of research topics used by Quickstep is based on
a part of ODP hierarchy (Netscape, 1998). The interest
value is computed using four sources of information:
•
user explicit feedback (research topic rated
as interesting or not interesting)
•
user implicit feedback (reading a paper, following
system recommendation)
•
interest spread over ontology (a parent concept receives
50% of its children’s interests)
•
time decay (the importance of a user activity for the
interest value is in inverse proportion with the number
of days passed since the moment of the activity).
The experimental evaluation of the Quickstep system
showed 7–15% greater acceptance rate of recommendations
utilising ontology-based user modelling over a traditional
overlay user modelling. Foxtrot is a modification of the
Quickstep system. It uses a more developed ontology of
research topics taken from the CORA project (McCallum
et al., 2000), which allows a more precise paper
categorisation and more advanced inference.
POV as individual conceptualisation
The second version of POV is a completely individual
knowledge model that can differ from the original domain
ontology in its structure and concept names. There are few
systems employing such a ‘pure’ implementation of POV.
OWL-OLM (Denaux et al., 2005b) is a user modelling
component used for elicitation of student knowledge
through interactive dialogs about the subject domain. Based
on her or his utterances a dialog agent builds a POV
representing an individual student’s conceptualisation.
It may contain relationships not existing in the domain
ontology, but reflecting student understanding of this part of
the domain. By comparing the domain ontology and the
49
elicited student conceptualisation, OWL-OLM identifies
the faulty relations, localises the problem, and infers the
pattern responsible for the error in the conceptualisation.
After that, the dialog agent chooses the next utterance,
which could be one of five types:
•
give answer to the user’s question
•
inform the user about an ignorance
•
agree with the user’s utterance
•
disagree with the user’s utterance
•
ask the user a question.
The user modelling in OWL-OLM is divided into a
long-term model and a short-term ‘conceptual state’.
The ‘conceptual state’ is built during a session; after the
session ends, OWL-OLM uses the ‘conceptual state’ to
update the long-term model. OWL-OLM recognises 17 types
of mismatches, such as “The same concept in the domain
ontology is not associated with this term”, or “The domain
ontology does not suggest that two concepts are related
by this property”. The current version of the system works
in the domain of simple Linux commands. However, the
choice of OWL as a representation model and the system
architecture make OWL-OLM truly domain-independent.
The authors of the mentioned OBIWAN project (Gauch
et al., 2003) have experimented with several approaches to
ontology-based modelling of interests of users searching
information on the web. Their implementation relying on
a simple overlay approach was already discussed in
Section 4.1.1. Another system developed in the same project
supports personalised web browsing based on user models
edited by the users themselves. The system asks the user to
specify a small hierarchy of topics representing their general
search interests. The user is limited neither in the choice of
text labels for the topics, nor in the structure of POV.
The system then uses POVs to navigate users to pages of
their interest. To personalise the web content, OBIWAN
associates the pages being browsed with the topics of
interest from the POV. As an intermediary it uses a
reference ontology based on the subject hierarchies from
Yahoo! (Kimmel, 1996), Magellan (McKinley-Group,
1997), Lycos (Lycos, 1999) and ODP (Netscape, 1998) and
multiple web resources assigned to the topics from these
hierarchies. OBIWAN uses an ontology mapping technique
to build the connection between the POV representing
the model of user interests and the reference ontology.
As a result, all web pages from the reference ontology are
linked to topics from POV and the user can browse them
in a personalised way.
4.1.3 Ontologies of user profiles
Sharability and reusability of user models require not only
standardised domain representation but also common
vocabularies for describing the user model itself. Therefore,
the design of ontologies describing sharable user profiles
has become an important research direction related to the
application of ontologies in user modelling. The term
50
S. Sosnovsky and D. Dicheva
‘user profile’ has been used traditionally to refer to a
compound knowledge structure comprising various static
information about users, such as demography, background,
cognitive style, etc. It is different from ‘user model’, which
usually represents a dynamic snapshot of a single aspect
of the user (conceptual knowledge, interests, preferences,
etc.). However, the distinction is rather conditional;
these terms have been often used as substitutes. User models
can have a compound structure and store static user
information, as user profiles can include a dynamic
component representing the current state of a certain user
characteristics.
A standardised user profile will make possible the
implementation of interoperable adaptive systems sharing
modelling information. Ontology-based user profiling is
especially important for systems reasoning across multiple
profiles (social adaptive systems) or for systems that can
benefit from complex inference on multiple ontologies
representing different knowledge (e.g., adaptive pervasive
systems using ontologies to model domain and context
knowledge).
Ontologies for learner profiles
The first steps towards user profile ontologies have been
reported in works of Murray and Mizoguchi. Murray
developed an ontology of a Topic, employed to assess all
aspects of student understanding of a single knowledge
element (Murray, 1998). Mizoguchi et al. (1996) developed
the task ontology for Intelligent Educational Systems (IES).
This ontology defines concepts involved in the design and
application of IES and has been intended to facilitate the
development of reusable IES components. One of the
top-level super-concepts is ‘Learner’s state’ that combines
various children concepts describing a student/learner in
IES. Chen and Mizoguchi (1999) proposed multi-agent
architecture of IES and described a scenario of
communication between a learner model agent and another
agent based on this ontology.
The transfer of a large part of computer user activities
to WWW along with the development of XML technologies
for metadata definition created both the need and the
possibility for development of metadata standards for
describing users on the web. There are two such metadata
initiatives developed solely for the field of education: IEEE
Public and Private Information (PAPI) for Learners
(IEEE-LTSC, 2001) and IMS Learner Information Package
Specification (IMS, 2001). Both standards provide XML
bindings for their tags.
W3C recommended a more elaborate framework for
metadata description – RDF. As a result, several RDF-based
metadata standards for describing users have been proposed.
The most widely-accepted metadata standard on the web,
DCMI has several elements for defining different categories
(roles) of users with respect to a document (DCMI, 1999).
W3C has issued RDF-binding for vCard (W3C, 2001a),
a standard for exchange of information about users in the
form of electronic business cards. Brickley and Miller
proposed FOAF – an RDF-based general-purpose model for
description of users on the web (Brickley and Miller, 2005).
The main motivation of the FOAF project is to provide
a metadata schema for implementation of social networks
on the SW.
Several attempts have been made to integrate previously
developed models into a single synergetic representation of
a user. Dolog and Nejdl designed an architecture for
ontology-aware AES on the SW. One of the ontologies
in this architecture is the learner profile ontology, developed
as a combination of IEEE PAPI and IMS LIP (Dolog and
Nejdl, 2003). This ontology was extended with new
concepts for the implementation of the TANGRAM learning
system (Jovanovic et al., 2006). Ounnas et al. (2006)
analysed the applicability of several existing approaches
(including the already mentioned IEEE PAPI, IMS LIP,
Dolog and Nejdl’s student profile ontology and FOAF)
to the development of Learner Profile in Social Software.
They decided to use the superset of these models taking
FOAF as a basis.
Ontologies for generic user profiles
The aforementioned projects have mainly focused on
representing information about learners. However, a number
of research teams try to address the more general problem of
developing a unified way of modelling a general user.
An important difference here is the undefined type of the
user and the context of system usage. The target user of
AES is a student acting in an educational setting with a
main goal to study. The modelling of a generic user or user
acting in arbitrary context should take into account the
context and the role of the user.
Niederée et al. (2004) proposed the Multi-Dimensional
Unified User Context Model (UUCM) and the creation
of a user-modelling server for cross-system personalisation.
To provide the maximum flexibility of modelling, UUCM
supports several modelling dimensions for modelling
different aspects of users’ state and activity: user context,
task, cognitive pattern, relationship and environment. The
structure of UUCM consists of two levels: the outer level is
a meta-model represented as an upper-level ontology
describing the different dimensions and relationships among
them. On the inner level, every dimension is represented
using its own ontology. Such composite architecture
allows adaptive systems communicating with UUCM to
concentrate only on the dimension they need. It also eases
the expansion of the model with new dimensions.
Heckmann developed UbisWorld, an infrastructure for
cross-system ubiquitous personalisation on the basis of a
set of shared ontologies (Heckmann, 2006). The adaptive
systems in UbisWorld communicate with each other by
means of messages, carrying user modelling information
expressed in UserML, an XML dialect described in
Heckmann and Krueger (2003). UbisWorld uses a large set
of ontologies describing various aspects of the reality that
can be employed for ubiquitous computing. Figure 7
presents these ontologies along with classes of objects
modelled by them. One of the components modelling the
current user’s situation is General User Model Ontology
(GUMO) (Heckmann et al., 2005).
Ontological technologies for user modelling
GUMO defines several hundred of concepts describing
various user characteristics from traditional ones, like
demography or knowledge to such, rather exotic, as facial
expression (see Figure 8). Several applications are currently
using this platform, including MobileMuseumGuide
(Kruppa et al., 2005) and Ubidoo (Stahl et al., 2007).
The Ubidoo system implements the functionality of a
personal organiser (Calendar + To-do list). The organiser
adapts its alarms and directions based on the changes in the
user’s location. If a user sets an alarm from one place, but
at the time of notification, she or he is in another location,
the time necessary to complete a task is updated according
to the distance to the target place (e.g., airport). The target
place necessary to complete a certain task can be updated
as well (e.g., if the task is to shop for groceries, Ubidoo can
recommend different grocery stores depending on the
current location).
Figure 7
UbisWorld ontologies (see online version for colours)
Source: Heckmann (2006)
Figure 8
Top-level concepts in GUMO (see online version
for colours)
Source: Heckmann (2006)
4.1.4 Ontologies for stereotype user modelling
A number of papers combine the use of ontologies with
user modelling based on stereotypes. The main premise
of stereotype user modelling is the existence of typical
user categories with generalisable characteristics (see
Section 2.2.3). The actual users coming to the system
are classified into one or several such categories, and their
user models inherit some parts from the corresponding
stereotypical profiles. The use of ontologies in this process
can be three-fold. First, the domain ontology can be used to
populate stereotype profiles; second, a single stereotype
51
profile itself can be implemented as an ontology; and third,
the ontologies can help organise the stereotype structure to
improve inter-stereotype reasoning.
Several systems applying ontologies for stereotype user
modelling rely on a domain ontology to populate the
characteristics of predefined stereotype profiles. The models
of individual users can be represented as overlays based
on the same domain ontology. When the individual user
model matches the preconditions of a certain prototype,
this prototype’s profile updates the user model. Ardissono
et al. (2003, 2004) describe a user-adaptive TV program
guide Personal Program Guide (PPG). PPG stores
stereotypical information about TV viewer preferences
for such categories of users as, for example, housewife.
The TV preferences themselves are modelled by a ‘general
ontology’, which includes hierarchy of TV programs from
broad like ‘Serial’ to more specific like ‘Soap Opera’,
“Sci-Fi Serial”, etc. The use of an ontology allows PPG
to structure the domain knowledge and effectively
map different TV program characterisations to the
ontological categories. Gawinecki et al. (2005) presents an
ontology-based travel support system. The system utilises
two ontologies for describing such travel-related domains as
hotels and restaurants. User modelling is consistent with the
traditional stereotype approach – particular preferences of
a user are inherited from stereotype profiles. The stereotype
profiles themselves are populated based on the
ontologies. This project signifies another benefit of using
ontologies – both ontologies have not been developed by the
authors but reused from relevant projects.
Recent advancements in the development of ontologies
for user profiles open opportunities for formalisations
of single stereotype models as separate ontologies.
An implementation of a stereotype as an ontology makes it a
sharable and reusable unit and can lead to the creation of
libraries of such modularised stereotypes. Both projects
mentioned above model the stereotypes as small ontologies,
however they do not base them on any existing initiative for
user profile ontologies. Gawinecki et al. (2005) design their
stereotype ontologies using RDF.
Some classic systems, like Grundy (Rich, 1979),
exploited hierarchies of stereotypes long ago. Such
structures facilitate stereotype management and make the
stereotype transition more effective. The more specific
stereotypes inherit characteristics from the more general
ones, hence a profile of a user switching, for example,
from a religious person to a Christian does not undergo
unnecessary updates. Systems implementing an ontology of
stereotypes get access to the pool of ontological
technologies for stereotype processing. Reasoning across
stereotypes as in the example described above can be
performed with the use of publicly available ontological
inference engines. Different systems employing local
ontologies of stereotypes can rely on ontology mapping
techniques to facilitate mediation of user models across
the systems. If a system decides to open the user profile to
the user, or even allow users to specify what stereotype they
think they belong to, ontology visualisation techniques
52
S. Sosnovsky and D. Dicheva
could be employed for implementing the corresponding
part of the system interface. Nebel et al. (2003) propose the
implementation of a database of reusable user ontologies as
a source for interoperable user information.
4.1.5 User modelling based on lexical ontologies
Thesauri and lexical ontologies, such as WordNet, allow
systems implementing traditional keyword-based user
modelling (see Section 2.2.2) to automatically enhance user
models with shallow semantics based on lexical relations
between words. This also provides a ready solution for such
NLP problems as polysemy (several meanings of the same
word) and synonymy (several words with the same
meaning). WordNet can considerably improve the quality
of keyword-based modelling of content, as its synsets
ensure unambiguous interpretation of NL terms. Systems
that implement keyword network modelling can utilise
relations between WordNet synsets, which allow them to
build more meaningful term networks than those based on
the term collocation.
One of the first adaptive systems exploiting WordNet
for the representation of user models is SiteIF (Stefani and
Strappavara, 1998; Strappavara et al., 2000). SiteIF is an
adaptive news recommender system “watching over the
user’s shoulder” while she or he browses the news items.
It uses the browsing history to predict users’ interests and
recommend relevant news. Unlike many other news
recommender systems that model news content by plain
keywords, SiteIF employs a set of techniques for word sense
disambiguation. For these purposes it uses MultiWordNet,
which is an Italian version of WordNet with some
extensions. Synsets in MultiWordNet are annotated with
semantic labels. A label represents one of 250 fairly large
categories (e.g., Architecture, Medicine, etc.). Based on
such an association not only the content of news items can
be modelled in terms of word senses, but they can be also
classified as belonging to one of the categories. A user’s
interests are modelled as a semantic net, where the nodes
represent WordNet synsets and the arcs – co-occurrences
of two synsets with arcs’ weights modelling co-occurrence
frequencies.
Semeraro et al. (2007) exploit a similar approach. They
introduce the JIGSAW algorithm that uses WordNet as a tool
for word sense disambiguation to move from flat
keyword-based user profiles to ‘sense-based’ user profiles.
JIGSAW adopts several previously developed algorithms to
appropriately disambiguate nouns, adjectives, and adverbs.
Its distinctive feature is the utilisation of the hierarchical
structure of WordNet. Consequently, not only it takes into
account the presence of relevant keywords in the same
synset, which helps to deal with problems of polysemy and
synonymy, but also tries to benefit of such relations between
synsets as antonymy (‘opposite’), hyponymy/hypernymy
(‘is-a’), and meronymy (‘part-of’). The evaluation of
JIGSAW has demonstrated that the accuracy of adaptive
recommendation driven by ‘sense-based’ user modelling is
considerably higher than that based on traditional keyword
user models. Authors report increase in both precision (8%)
and recall (10%).
The same research team also applied WordNet-based
user modelling for the implementation of a hybrid
recommender system (Lops et al., 2007). Hybrid
recommender systems (Burke, 2007) combine several
recommendation approaches in attempt to compensate
weaknesses of one method by the strengths of another.
For example, the quality of recommendation based on
collaborative filtering suffers from the so-known ‘cold start’
problem. It is hard to find like-minded users for a new user,
who just has joint the system and have not yet generated
enough evidence for the system to make a valid prediction.
Content-based recommendation can help to remedy this
problem, as it is based on the content model of information
items and can start working immediately after a user has
made his first move (e.g., has rated the first item). However,
the quality of content-based recommendation strongly
depends on the quality of item models. Lops et al. (2007)
apply WordNet synset to model the content of movie
descriptions. The descriptions contain several slots: title,
genre, cast, short summary, etc. Collaborative filtering is
applied to compute similarity between users based on the
users’ ‘sense-based’ profiles. The evaluation has shown
statistically significant improvement in the predication of
like-minded users that this approach brings comparing to
the traditional collaborative filtering.
WordNet, has proven to be a useful tool for word sense
disambiguation and shallow semantics modelling in many
applications (Rosenzweig et al., 1999). Since its first
release, its authors have been improving its structure and
advancing its semantic capabilities (see, for example,
Harabagiu et al., 1999). Starting from version 2.1, WordNet
allows to distinguish such ontological categories as classes
and instances (Miller and Hristea, 2006), i.e., it is capable to
differentiate between relations ‘is-a’ and ‘subClassOf’.
This feature allows systems using WordNet to move
one step closer towards an automatic generation of semantic
content profiles from unstructured text.
4.2 Ontologies for user model elicitation
This section describes projects that can be roughly divided
into two categories: projects using ontology-based
knowledge acquisition techniques for unobtrusive user
modelling and projects applying ontology visualisation for
open, editable, and interactive user models.
Unobtrusive user modelling utilises a set of technologies
from such fields as machine learning, data mining, and
information storage and retrieval. As a result, adaptive
applications trying to elicit user information implicitly,
in unobtrusive manner, often face challenges arising due to
the conflicts between the computational demands of
machine learning and data mining algorithms, on one side,
and the interactive and dynamic nature of the user
modelling task, on the other. These challenges have been
nicely summarised by Webb et al. (2001) (Section 2.3.2).
For some applications, the problem can be aggravated by
Ontological technologies for user modelling
53
additional requirements of the adaptation task, such as
explanation ability and semantic interpretability of user
modelling assumptions. Recent research efforts on
application of ontological technologies for unobtrusive user
modelling demonstrate the potential to partially overcome
some of those difficulties.
Systems allowing users to view, explore, and modify
their user models can benefit of using ontology-based
visualisation. An ontology can help to structure and
externalise the conceptual knowledge in the domain. Hence,
the utilisation of an ontology as a cognitive component can
help an open user modelling system to communicate domain
knowledge to the user in a natural way.
In this section we discuss using ontological technologies
for population of user models with individual information
and for mining user models with individual structures for
adaptation purposes. We also overview works on using
ontology visualisation to open user models and help users
to explore and modify their models.
traditional semantic indexing with automatic capabilities.
For the adaptation community, it opens an opportunity to
solve the classic problem of open-corpus personalisation.
The IR and DM community appreciates ontology-based
content annotation for the generation of semantically rich
models. The main component of user models developed
in this field is traditionally a flat list of user transactions.
Therefore, the projects originated in this community
often refer to the problem as a semantic enrichment of
usage logs. Ultimately, both viewpoints represent the same
functionality – automatic generation of semantically rich
content and user models. The difference is in stressing
‘automation’ vs. ‘semantics’.
Below we first discuss projects addressing the
problem of open-corpus personalisation with the help of
ontology-based automatic content annotation and then
review works moving from the traditional keyword-based
user modelling to the concept-based approach through
semantic enrichment of usage logs.
4.2.1 Ontology-based population of user model
4.2.2 Ontology-based open-corpus personalisation
One of the major challenges of content-based adaptation is
the efficient modelling of the content in terms of domain
elements. Two main approaches developed over the years
address this task from different angles. The classic KR
methodology requires building an external model of the
domain semantics and manual indexing of content items
with elements of this model. The resulting representation
allows associating users’ actions (solving a problem,
accessing a document, etc.) with knowledge underlying the
accessed information items and implementing effective
adaptation strategies. However, this authoring approach is
time-consuming and expensive, and not applicable for many
applications operating in the setting of AW.
Another methodology that originated in the field of
information retrieval and data mining does not require
the creation of sophisticated models, nor does it need the
intervention of domain experts to provide manual content
indexing. Instead, it relies on the automatic extraction of
keyword vectors from the documents’ content. This
approach has been successfully applied for a number of
tasks requesting automatic modelling and effective retrieval
of textual information (e.g., web search); however, it has its
own problems as well. The keyword-based modelling is not
capable to deliver the elements of domain semantics. As a
result, the adaptation strategies that require deterministic
reasoning do not work. The alternative machine learning
approaches impose additional requirements and are not
always applicable in adaptive applications.
Recently
developed
ontology-based
annotation
techniques look promising for building a bridge between
semantically rich concept-based models and automatically
generated keyword-based models.
Three different communities contribute to this field of
research forming two main perspectives on the problem.
Ontologies and traditional adaptation technologies share the
heritage of AI and classic KR. From their point of view,
the ontology-based annotation helps to augment the
The observed interactions of a user with an adaptive
system’s content may result in a collection of detailed
transactional logs. The challenge for the adaptive system is
to interpret this data and utilise it for adaptive tailoring
of future user interactions. Traditional content-based
adaptation relies in this process on composite domain
models and associations between content items (e.g., tutorial
pages, problems, etc.) and domain elements (concepts).
It allows adaptive systems to reason in terms of domain
semantics instead of content, and to generalise user
characteristics based on their history of interaction with the
system. For the closed-corpus applications operating on
restricted, relatively small sets of content items, the
association of such items with domain elements can be done
manually with the help of advanced authoring tools.
However, AW user modelling systems usually operate in
open-corpus settings; they have to deal with virtually
unlimited or dynamic sets of documents and cannot benefit
of manual authoring. Consequently, when the corpus size
goes beyond a manually manageable threshold (as in
adaptive web search) and/or the content refreshes on a
regular basis (as in adaptive news recommendation),
content-based adaptation requires automatic support for
semantic indexing. A promising direction to remedy this
problem is to apply ontology-based annotation techniques
for automatic or semi-automatic indexing of the content
to be adapted.
Open Adaptive Hypermedia System (OAHS) (Henze
and Nejdl, 2002) is one of the first attempts to apply
ontologies for the problem of modelling and adaptation
in an open-corpus environment. The authors do not specify
however how OAHS supports semantic mark-up of
unrestricted content. They discuss mechanisms for
estimating users’ knowledge and implementing adaptation
strategies in the conditions of an open-corpus document
space.
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S. Sosnovsky and D. Dicheva
A number of projects adopting this approach to
personalisation come from the field of ontological research.
One of them is Conceptual Open Hypermedia Service
(COHSE), a joint project between Sun Microsystems and
University of Manchester (Bechhofer et al., 2003;
Yesilada et al., 2007). COHSE is based on the original idea
of distributed link services (Carr et al., 1995) working as
intermediaries between web clients and web servers and
augmenting web documents with dynamic links.
Architecturally COHSE works as a proxy. It intercepts a
client’s HTTP request, retrieves the original HTML
document, enriches its content with new links and delivers
the modified document to the user’s browser. The extra
links added by COHSE connect key pieces of text in the
original document with relevant HTML context elsewhere.
This knowledge-driven linkage is performed on the basis of
an ontology, serving as a source of consensual domain
semantics. The COHSE components apply ontology-based
annotation techniques to associate automatically pieces of
documents with ontology concepts. When a document is
requested, its annotations act as links to concepts and thus to
other documents annotated with the same (or relevant)
concepts. Currently COHSE does not support user
modelling and user-based adaptation of the content. The
proposed links are based ultimately on the hidden domain
semantics and do not take into account individual browsing
history or other sources of user-related information.
However, recent publications on COHSE report interest in
this research direction and list it in the plan for future
development.
Similar approach is implemented in the project Magpie
(Domingue et al., 2004; Dzbor and Motta, 2007). Unlike
COHSE, Magpie works on the client side as a browser
plug-in. It analyses the content of the HTML document
being browsed on-the-fly and automatically annotates it
based on a set of categories from an ontology. The
resulting semantic mark-up connects document terms to
ontology-based information and navigates a user to content
describing these terms. For example, if Magpie recognises
that a particular phrase is a title of a project, it populates a
menu with links to project’s details, research area,
publications, members, etc.
By combing the capabilities of systems like COHSE and
Magpie with the architectural solutions provided by
OAHS, one can obtain a complete functionality necessary
for open-corpus personalisation. Several systems
attempted to solve this problem for the task of adaptive
recommendation.
The systems Quickstep (Middleton et al., 2001) and
Foxtrot (Middleton et al., 2003) mentioned in Section 4.1.2
apply a similar set of technologies for implementation of
adaptive recommendation of research papers in open-corpus
settings. Newly found papers are automatically classified as
belonging to one of the topics from the research topic
ontology. If the topic matches some of the interests in a user
profile, the paper is recommended to the user. The profile
is updated based on the user’s activities (browsing
papers, downloading papers, rating papers, following
recommendations).
Another example of fully implemented user modelling
and adaptive recommendation for unrestricted content is
provided by the system MyPlanet (Kalfoglou et al., 2001),
created in the Knowledge Media Institute (KMi) of Open
University (UK) by the same research team who developed
Magpie. MyPlanet provides personalised access to KMi
corporate news e-stories based on user preferences. On the
meta-level user preferences belong to one of the following
ontologies: research areas that are investigated in KMi;
research themes that are investigated in KMi; organisations
that KMi collaborates with; projects in KMi; technologies
used in KMi; application domains that are investigated in
KMi; people - members of the KMi lab. The e-story
submission is done by e-mail. Once submitted, stories are
automatically annotated by ontology concepts using one of
40 predefined event templates. For example, for the event
“visiting-a-place-or-people”, the template looks as follows:
[_, X, _visited, Y, Y, from, Z, _]. Here X is an entity capable
of visiting, Y is a place being visited, and Z is for dates of
the visit. When MyPlanet recognises a story matching this
template, it extracts participating entities, associates them
with ontology concepts and annotates the story accordingly.
When selecting e-stories to present to the user, MyPlanet
tosses queries to the ontology and retrieves story items
matching the user profile. If the system finds available
stories annotated with concepts, which are neighbours of
some of the concepts defining user’s interests, such stories
will be also retrieved.
4.2.3 Semantic enrichment of usage logs
The same problem can be addressed differently, from
the viewpoint of traditional data mining and web-based
recommender systems. Data mining research usually deals
with large sets of documents; consequently, the focus here
is not on the size of the annotated corpus or the automatic
generation of content models, but on the semantic
annotation. Conventional approaches to adaptation in this
field do not presume semantic annotation of content items,
therefore the papers applying ontology-based annotation
for adaptation in this field talk about semantic enrichment
of transactional logs or semantically enhanced
recommendation.
Oberle et al. (2003) specifies a general algorithm
employed by adaptive systems relying on mining of
conceptually enriched usage data:
•
Description of raw data: the description of the
transaction, which is generally a user visit or session,
as a set or sequence T of URLs u: T = {ui} or T = [ui]
•
Mapping: the mapping of URLs to objects
that are meaningful within the application domain
(e.g., concepts from an ontology), with m(u) = O
describing the mapping of URL u to a set of objects O
Ontological technologies for user modelling
•
Mining: the identification of patterns in the set or
sequence of these meaningful objects, i.e., in the
transformed transaction T′ = {UiO(ui)} or T′ = [O(ui)].
The difference of this approach from the traditional data
mining for user modelling is the utilisation of semantic
information in the user modelling and adaptation processes.
This enables discovery and recommendation of usage
patterns, inferred on the basis of the semantic equivalence
of pages. It also allows adjustment to the new and/or
dynamic content and changes in the structure of a website.
Semantic links do not break (however, preprocessing stage
should be repeated).
For example, the SEWeP web recommender system
(Magdalini et al., 2004) performs the following routine to
issue more meaningful recommendations, based on the
content semantics. All web pages are represented as
weighted term vectors; the terms are mapped to the concepts
of a domain taxonomy with the help of WordNet as the
translation mediator. As a result, the web pages are
represented as weighted vectors of taxonomy concepts.
After that, all documents are combined into semantically
coherent clusters based on the obtained vectors; taxonomic
relations are used to cluster together documents described
by close concepts. Correspondingly, SEWeP augments
web-logs of user activities with conceptual information for
the visited pages. Association rules mined from the
web-logs represent causative relations between concepts.
These rules are used for generating recommendations to
users who have generated a sequence of transactions
corresponding to the left part of a rule.
Similar solution is implemented in Dai and Mobasher
(2002). The authors do not concentrate on the details of
associating semantic metadata with web pages. The focus is
instead on developing a framework for discovery and
representation of complex domain-level aggregate profiles
based on the usage data and the semantics underling the web
pages. The domain is represented as a heavy-weight
ontology with classes and objects combined in a complex
network through typed attribute relations. As a result, much
richer information becomes available for the inference of
semantic user profile.
Zhang et al. (2007) developed Semantic Web Usage Log
Preparation Model (SWULPM) that helps recommender
systems to integrate usage data and domain semantics
represented by an ontology. The evaluation of the
recommendation quality based on SWULPM demonstrated
about 10% accuracy increase in comparison with
recommendations based solely on the usage history taken
from shallow sessions.
Utilisation of domain ontologies enables adaptive
recommender systems to combine different (somewhat
orthogonal) recommendation strategies: the search for
possible items to be recommended can be performed both in
the previously collected usage history (collaborative
filtering approach) and in the rich domain ontology
(knowledge based approach) (Mobasher et al., 2004).
55
4.2.4 Ontology-based learning of user model
structure
The previous section overviewed systems enriching
content with semantic information and using ontologies
for deriving generalised user characteristics. In these cases,
the underlying ontology defines and restricts the structure of
the user model; the modelling system solely populates it
with information reflecting the user’s idiosyncrasies.
A number of recent research projects attempt to achieve a
more challenging goal of mining the structure of the user
model itself based on the user’s activity. To facilitate the
complete creation of user models from the observed implicit
user behaviour, these systems apply a set of techniques
related to ontology learning and ontology mapping. User
models that are not restricted by a domain ontology have a
potential to define fully individualised views on the domain
semantics, which can have their own structure and even
vocabulary. We have discussed partly such models in
Section 4.1.2. Not all approaches described here address the
challenge of creation of a complete ontology-based user
model; some works only report preliminary progress in this
direction.
Zhou et al. (2005) described a system generating
personal “web usage ontologies” based on transactional
logs. The multi-stage mining process the authors propose
utilises log analysis, fuzzy logic, formal concept analysis,
and graph models. After a log’s pre-processing (including
transaction filtering, session separation, etc.), the so-called
web-usage context is calculated for every session – the
sessions are annotated with fuzzy categories representing
the time of the session and a broad topic browsed by the
user during the session. The next two steps – construction
and pruning of web usage lattice – result in a graph
representing all important navigation states during the
session, connected to each other if a transition between
corresponding states is possible. The importance of the state
is defined on the stage of pruning based on the support and
confidence values for the state. Finally, the pruned web
lattice is used to generate a “web usage ontology” of the
user represented in OWL. As a future research direction,
the authors plan to implement an adaptive service utilising
the described approach. Unfortunately, the resulting
knowledge structure can hardly be named an ontology in the
conventional sense, as it does not represent the declarative
semantics of a domain. The model rather contains a set of
mined navigation patterns and transition rules. Nevertheless,
this project demonstrates a potential for extraction of rich
models based on the deep web usage mining.
Several projects, originated in the field of user
modelling, apply ontology learning techniques to learn the
structure of the domain ontology and then use it for
adaptation purposes. For example, the ontology learnt by
MECUREO (Apted and Kay, 2004) (which we mentioned in
Section 3.4.2) has been applied in a number of adaptive
systems (for example, for visualisation and navigation
(Apted et al., 2003a) and for learning object metadata
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S. Sosnovsky and D. Dicheva
generation (Apted et al., 2004)). Zouaq et al. (2007) reports
initial progress on the Knowledge Puzzle system that tries
to learn domain ontologies from unstructured text. The
authors plan to utilise the learnt ontologies for the purpose
of adaptive instruction.
The Willow system (Pérez-Marín et al., 2006a, 2006b,
2007) is the only known to us example of learning the
conceptual structure of a user model from unstructured text.
Willow is an automatic assignment grading system assessing
free-text students’ answers. When a teacher creates
assignments in Willow, she or he needs to specify to which
of the top-level domain categories the assignment belongs.
Every assignment also requires at least three sample
answers provided by different experts to build a reference
answer model and take care of possible rephrasing. When a
student submits her or his own answer to a problem, Willow
extracts the concepts covered in the answer and matches
them against the concepts learnt from the reference model.
As a result, it creates an individual conceptual model of a
student reflecting the correctly reported knowledge and
possible misconceptions. The evaluation of Willow shows
that there is a statistically significant correlation between the
grades given by the system and the actual grades received
by the student in class.
A relevant approach is implemented in the OBIWAN
project, previously mentioned in Section 4.1.2. It does not
mine the user’s POV from her or his actions, instead,
it allows the user to create an individual model of the
domain and maintains AW browsing based on this model
(Gauch et al., 2003). As already mentioned, the OBIWAN
user can specify a small hierarchy of topics representing her
or his interests, using any topic names and structuring the
model in any way she or he prefers. To create a common
ground for various interest models OBIWAN uses a large
reference ontology aggregating several available web
directory hierarchies. The reference ontology concepts come
with web pages attached to them. OBIWAN analyses these
pages to extract keyword profiles for the reference ontology
concepts. When users define their interest model, the system
asks them to provide several sample web pages for every
topic in the individual model. OBIWAN uses this data to
map concepts from the reference ontology to the concepts in
the individual user interest profile. The found mappings
allow OBIWAN to model users’ interests and navigate a user
based on the newly created topic hierarchy that reflects a
completely individualised view on the domain not restricted
by a pre-existing knowledge model.
A set of systems previously mentioned in Section 4.1.5
utilises lexical ontologies like WordNet for eliciting
sense-based vs. keyword-based user models. Such projects
as SiteIF (Stefani and Strappavara, 1998; Strappavara et al.,
2000) and JIGSAW (Semeraro et al., 2007) pre-process
keyword-based models by linking synonymous words to the
same synsets and disambiguating senses of polysemious
words. The synsets in the models can be connected
by WordNet relations (antonymy, hyponymy, and
meronymy). WordNet extensions, such as MultiWordNet,
allow automatic attribution of a synset to a certain top-level
domain category. The resulting models represent
individualised profiles of users’ characteristics; their
structure depends on the history of users’ actions (e.g.,
in SiteIF the model of a user’s interests is based on the news
items a user has browsed).
4.2.5 Ontology-based open, editable and interactive
user modelling
In addition to powerful representational capabilities,
ontologies can provide a useful way for presenting a domain
knowledge to the user in a comprehendible and navigable
form. Several adaptive systems have exploited this feature
to visualise the domain semantics and directly communicate
to the users the current state of their user models.
Two dominant ontology visualisation layouts are used
by these systems – a class hierarchy and concept
maps – however, some other techniques have been tried as
well. Some systems use ontologies solely as a navigation
aid, others implement ontology-based interfaces to open
user models to the users, and finally, a limited set of
systems exploit ontology visualisation to elicit user
characteristics in an interactive manner.
The OWL-OLM system (previously discussed in
Section 4.1.2) provides an example of using ontology
visualisation to elicit users’ knowledge of domain concepts
and relations between them (Denaux et al., 2005b).
OWL-OLM organises the process of student knowledge
elicitation in the form of interactive dialog between the
student and the Dialog Agent. The Agent analyses the
student knowledge model and chooses a concept, not known
by the student, or for which the student demonstrated a
misconception. The Agent maintains the dialog to assess
student knowledge of the entire neighbourhood of the target
concept, including the concept attributes and its relations
with other concepts in the domain ontology. Figure 9 shows
a part of the dialog about the file system in Unix. The
Dialog Agent states its utterances in a text form. The student
creates response utterances using a graphical editor thus
building gradually the target part of the ontology.
The system converts student’s actions into OWL statements
and verifies them against the domain ontology. If it finds a
mismatch between the student model and the domain
ontology, the dialog is altered to resolve this mismatch. The
dialog ends, when the Dialog Agent fills that it has properly
assessed all knowledge relevant to a particular concept,
or it can be ended by the student. The important feature of
the OWL-OLM approach (besides the interactive graphical
ontology-driven dialog-based elicitation of student
knowledge) is the employment of ontologies as a learning
component. By creating and browsing their own versions
of the domain ontology in the form of concept maps,
students learn directly the conceptual structure of the
domain.
The Verified Concept Mapping (VCM) system
(Cimolino et al., 2004) follows a similar approach. Students
working with VCM demonstrate their knowledge in the
domain by building its concept map. The student interface
Ontological technologies for user modelling
of VCM consists of a working space, where students draw
their maps, a list of predefined concepts and a list of
predefined relations (Figure 10). The teacher authoring the
problem should populate these lists. If a student feels that
some of the concepts and/or relations are missing, she or he
can add their own concept map elements. After the student
submits the map, VCM checks its correctness against
the etalon map created by the teacher, according to the
assessment rules specified also by the teacher. If the
student’s map violates any of the rules, the system generates
appropriate feedback. The correct passing of certain
requirements is also included in the feedback. Application
of concept mapping to elicit conceptual structures of users’
domain knowledge is a popular approach (see also the
COMPASS system (Gouli et al., 2004)). The difference of
VCM is in the utilisation of concept maps for population of
an ontological student model, which is verified against the
domain ontology.
Figure 9
Ontology-based elicitation and visualisation of user
models in OWL-OLM (see online version for colours)
Source: Denaux et al. (2005a)
Figure 10 POV assessment by Verified Concept Mapping
Source: Cimolino et al. (2004)
COnceptual MOdel Viewer (COMOV) (Pérez-Marín et al.,
2007) visualises models of student knowledge extracted by
the Willow system (Section 4.2.4). Unlike many other
57
systems, COMOV opens student models not for the students
to reflect upon them, but for the teacher to monitor
student performance. COMOV can present the model of a
particular student and the aggregated model of the class.
It experiments with four different kinds of visualisation:
concept map, table, bar chart (or skillometer) and text
summaries (see Figure 11). The node colours represent the
conceptual knowledge of the student or the class on the
scale from red (no knowledge) to green (maximum
knowledge). The knowledge levels of leaf-concepts are
aggregated to propagate knowledge for top categories
in the ontology. The evaluation of COMOV with real
teachers demonstrated positive attitude towards the system.
Teachers appreciated the opportunity to observe students’
performance on the concept level. The most popular
visualisation layout was concept maps. This finding is
consistent with a similar experiment done for the FlexiOLM system. According to the student evaluation of several
layouts for opening conceptual knowledge models, concept
maps and the lecture structure have been found most useful.
Figure 11 Student model visualisations in COMOV (see online
version for colours)
Source: Pérez-Marín et al. (2007)
The VlUM project (Uther and Kay, 2003; Apted et al.,
2003a, 2003b) proposes an interesting solution to the
problem of visualisation of large ontology-based user
models. When the size of the domain ontology is above
several hundred concepts, visualising the user model can
become a challenging task, as the capabilities of the screen
as well as the perception capabilities of the users are
limited. VlUM visualisation (see Figure 12) allows users
both to have a snapshot of the entire model and to
concentrate on the concept that is currently in focus.
The visualisation applet occupies a fixed area on the screen
and can present as many concepts as needed. When a user
clicks on a concept, VlUM highlights the concept by
increasing the label font and reserving some space around
it. The related concepts are highlighted in the same way,
but with less magnitude. The rest of the concepts, not
relevant to the current user focus, are presented in a small
font and very close to each other. When a user browses
the hierarchy, all concepts are clickable, and once a user
clicks on a concept, the focus shifts to it. The relevant
positions of concept labels represent the distance between
58
S. Sosnovsky and D. Dicheva
the concepts in the model; the left indentation shows the
level of the concept within the hierarchy.
Essentially, the VlUM visualisation is a smart
modification of the traditional class hierarchy, which allows
keeping all concepts on the screen and accessing them in
one click. The concept labels’ colour represents the user’s
interest in the concepts and changes on the scale from red
(minimum) to green (maximum). In addition, VlUM allows
users to filter out some relation types in the original model
and to adjust the colour-switching thresholds and the depth
of the relevant concepts highlighted together with the focus.
Figure 12 Visualising conceptual user models in VlUM
(see online version for colours)
The Foxtrot recommender system opens for a user not only
the current state of her or his interest model, but the
evolution of the user’s interest in a topic. The visualisation
is implemented as a time chart (see Figure 14). The user can
view several interest profiles at the same time. Foxtrot also
gives the user the opportunity to modify the profile chart,
which results in the modification of the actual user profile
and reinforces the modelling mechanism. The evaluation
of this approach showed that direct profile modification
helped Foxtrot to improve the accuracy of recommendations
during the first week. These results are not surprising,
as in the early stage Foxtrot has not accumulated yet enough
information about users based on their activity, therefore the
direct profile modification provides the Foxtrot’s modelling
component with very useful information. Consequently,
the modification of the current levels of user interests would
require less effort on the user’s side and probably would
have the same effect. The usefulness of the modification of
user profile’s history and applicability of this approach in
specific settings (system tasks, user population, etc.)
requires more research.
Figure 14 Use profile visualisation in Foxtrot (see online version
for colours)
Source: Apted et al. (2003b)
Some systems exploit different ontology visualisation
techniques. For example, QuickStep allows a user to
navigate an ontology of topics using a set of pop-up menus
(see Figure 13). If the user does not agree with the result of
the automatic annotation provided by QuickStep, she or he
can manually modify the annotation by choosing a different
topic from the menu hierarchy. This will influence the
calculation of the topic interests for the users based on their
activity with the current paper.
Figure 13 Quickstep allows users to browse the ontology and
change the topic associated with the paper (see online
version for colours)
Source: Middleton et al. (2004)
Source: Middleton et al. (2004)
4.3 Ontology-based user model interoperability
Nowadays, WWW becomes the major infrastructure for
information and service delivery, including adaptive
services. Many web systems collect data about their users,
maintain models of certain user aspects and tailor their
behaviour accordingly. The situation, when the same user
works with multiple adaptive systems is very common.
For example, the same user can receive personalised
recommendations from Amazon.com when shopping
for books and from Blockbuster when renting movies.
The quality of recommendations will highly depend on the
amount of information both systems have about the user;
and, in many cases, the information collected by one system
can be relevant for the judgements made by another.
For example, if the Blockbuster recommender was aware
that the user bought on Amazon.com all books about
Harry Potter it would have the evidence that the user might
Ontological technologies for user modelling
be interested in renting a new Harry Potter DVD, and
probably some other DVDs with Fantasy movies.
Unfortunately, since the systems cannot get an access to
each other’s user model collections, they have to extract
user interests on their own. To make cross-system
adaptation possible, mediation of user models between the
systems is necessary. This task involves several important
issues including privacy, security, trust, user identity, etc.
However, probably, the central challenge here is how to
translate between the user models built by the different
systems. In this section, we discuss the problem of user
model semantic interoperability and exemplify some
solutions for this problem provided by ontological
technologies.
The task of interfacing two user models involves
the resolution of modelling differences or discrepancies.
There are four main sources of potential discrepancies
between user models collected by different systems:
•
different user modelling approaches
•
mismatches in domain models
•
different representations of user profiles
•
scale discrepancies.
59
provided by the users. The translation of such models
require completely different solutions, such as the
ones discussed in Berkovsky et al. (2006), where the
authors describe mediation between a case-based and a
keyword-based models.
Here we do not analyse the problem of interoperability
of non-semantic user models and focus on projects
exploring the opportunities ontologies provide for
interfacing information about the same user collected
by different adaptive systems.
4.3.2 Resolution of domain discrepancies
Overlay user modelling uses a conceptual domain
representation as a basis for structuring user information.
However, the same domain (or related parts of intersecting
domains) can be modelled differently in different adaptive
systems. For example, Amazon.com and Blockbuster can
model customer interests as overlays over ontologies of
book and movie genres, correspondingly. Figure 15
demonstrate how similar parts of these ontologies can differ
from one another:3
•
The same concepts can be named differently,
for example, the concept label ‘Fairy Tale’ in the book
genre ontology and the concept label ‘Fantasy’ in the
movie genre ontology.
•
The same concept labels can model different concepts.
For example, the concept label ‘Fiction’ models
different genres in both ontologies.
•
The structure of inter-concept relations can be different.
For example, the concept ‘Kids’ in the book genre
ontology is a top-level category, while in the movie
ontology is a sub-concept of the concept ‘Family’.
•
The granularity of modelling can be different, e.g.,
several concepts in the movie genre correspond
to the single concept ‘Fiction’ in the book genre
ontology.
•
The focus of modelling can be different, which may
result in any of the discrepancies mentioned above.
For example, the concept ‘Fairy Tale’ in the book
ontology is a genre for Kids. At the same time, the
movie genre ‘Fantasy’, the most relevant to
‘Fairy Tale’, is a sub-concept of ‘Fiction’ in the movie
genre ontology. This discrepancy reflects a different
view on the modelling of similar semantics. The book
ontology stresses the strength of the fact that the
majority of fairy tale readers are kids, while the movie
ontology models the fact that Fantasy ‘is-a’ Fiction.
The following subsections briefly analyse the nature of
these discrepancies and potential ways to solve them.
4.3.1 Integration of different user modelling
approaches
As already mentioned, adaptive systems can represent
information about their users in many different ways,
including concept-based, keyword-based, stereotype,
constraint-based,
Bayesian
networks,
collaborative
filtering, item-based, case-based, etc. (see Section 2.2).
Several projects address the task of employing several
user-modelling approaches in a single hybrid system to
serve better adaptation (see Burke (2007) for a review
of hybrid adaptive recommenders). However, the problem
of translation between two fundamentally different
representations of a user created by systems that understand
only one of them is a great challenge. Ontologies can
provide a solution in a limited number of cases. For
example, ontology-based knowledge acquisition methods
can be employed for transforming keyword-based models
into concept-based; lexical ontologies, such as WordNet,
can also facilitate the extraction of semantic information
from keyword vectors. Suraweera et al. (2004) introduce the
project ASPIRE that utilises ontologies in constraint-based
tutors.
However, not all user-modelling approaches require
representation of domain semantics, or semantics of user
profiles. For example, the main component of user models
for adaptive systems exploiting social filtering or
item-based filtering is the matrix of interest ratings for items
Finally, the representation formalisms used by the systems
to express the domain model can be different. One system
can store it in a relational database, yet another can
externalise it as a set of XML files.
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S. Sosnovsky and D. Dicheva
Figure 15 Example of domain discrepancies (see online version
for colours)
(Denaux et al., 2005b). Figure 16 demonstrates the
OntoAIMS architecture. The OWL-OLM system supports
interactive elicitation of learner knowledge (see Sections
4.1.2 and 4.2.5). Both the domain ontology and the elicited
learner model are presented in OWL and available to the
AIMS system, which provides the learner with adaptive
access to available educational content.
Figure 16 OntoAIMS integrated architecture
The domain discrepancies lead to different interpretation of
identical user actions. For example, according to Figure 15,
the interest model of a buyer of books ‘The Firm’ and
“Harry Potter and…” will differentiate from the model of a
user who rented movies adapted from the same books,
although these actions indicate interests in similar content.
While a user is modelled within a single system,
her or his model is consistent with the underling domain
representation. However, when we try to merge two models
to benefit of relevant user activities across the systems,
domain discrepancies prevent from coherent interpretation
of identical user actions performed with books and movies.
Ontologies provide a principle solution to the problem
of domain model discrepancies. In our example, the
presence of a publicly available ontology of genres
represented in a sharable format and adopted by the
developers of both systems would bring the user models of
both systems to a common ground. Probably, the most
successful initiative to develop a single domain ontology
shared by a large community of experts is the Gene
Ontology project (Ashburner et al., 2000). This ontology is a
ready-to-use instrument for modelling truly consensual
knowledge in the area of gene research. Although we are
not aware of existing adaptive systems using this ontology
as a domain model, the developers of future systems in that
field should consider its employment.
In the area of user-adaptive systems, the UbisWorld
project mentioned in Section 4.1.3 is an example of
centralised ontology development. The set of UbisWorld
ontologies models various aspects of ubiquitous computing
for the development of adaptive ubiquitous applications
understanding each other and providing consistent
adaptation for the same user based on the shared knowledge
about her or him. Another global ontology that can be useful
as a mechanism of domain discrepancy resolution is
WordNet. As mentioned before, several adaptive systems
use WordNet to remedy such problems of keyword-based
models as word polysemy and synonymy. If a single user
has worked with several such systems, it should be possible
to merge the WordNet-based user models of these systems
to obtain a more complete profile of the user.
OntoAIMS is an example of integration of two separate
adaptive systems, based on a shared domain ontology
Source: Denaux et al. (2005b)
AIMS structures the material as a hierarchy of tasks, where
every task has a set of associated domain concepts. The
model supplied by OWL-OLM is utilised by AIMS for
adaptive recommendation of a next task to the learner (once,
the learner masters all prerequisite concepts) and for support
of adaptive browsing through the material related to the
current task. While learners browse task-related material
and definitions of relevant concepts in AIMS, both systems
update their models as well. Hence, OntoAIMS provides
full integration of two AES based on shared user model.
A common domain ontology is used as a basis for user
model mediation.
Most often however systems operating in the same
domain rely on different ontologies. In such cases,
ontology-mapping techniques can help in setting common
understanding of the domain semantics. Once the mapping
is found, the systems should be able to align their domain
models and mediate users’ information. We are not aware of
existing projects utilising general automatic ontology
mapping techniques for translation of user models. A few
papers considering the problem of cross-system
personalisation based on domain ontology mapping either
rely on manual mapping or assume the existence of a
reference ontology that can help to map the top concepts
of systems’ domain models.
An example of the first approach is the Medea learning
portal (Trella et al., 2005). Medea provides students with
access to multiple educational systems from a single point.
To maintain consistent adaptivity across several applications
Medea needs a mechanism for mediating user models
between the applications. This task is achieved by manual
mapping of the domain models of the adaptive systems
to the central domain model of Medea. Hence, a student’s
knowledge assessed by any adaptive educational system
accessed through Medea is translated into Medea’s domain
representation and stored in the central student model of
Medea. If a system needs to obtain the current state of
the student’s model to appropriately tailor its behaviour to
her or his knowledge, it asks Medea. Medea retrieves the
modelling information, translates it into the system’s
representation and reports it to the system.
Ontological technologies for user modelling
The same approach has been also implemented in the
M-OBLIGE model (Mitrovic and Devedzic, 2004).
The authors consider a problem of mediating knowledge
models of the same learner between several intelligent
tutors teaching different aspects of database management
(entity-relationship diagrams, SQL, database normalisation).
Although the domains of these tutors are different,
they share several concepts. For reasoning across tutors’
local ontologies and mediating users’ knowledge models
M-OBLIGE introduces an ontology processor. The ontology
processor uses the global ontology (e.g., the general
ontology of data models) to link tutors’ local ontologies and
support cross-tutor personalisation.
Although these approaches potentially ensure high
quality of user model mediation, they have serious
shortcomings. Medea’s approach requires manual mapping
(by a domain expert) of the central domain model to the
model of a new adaptive system. Since the size of domain
models can exceed several hundred concepts, the task
can be very time- and expertise consuming and thus a
non-realistic scenario for multiple systems for multiple
domains. The M-OBLIGE framework assumes the existence
of a global ontology linked to the local ontologies of the
tutors. Such commitment is only possible in the settings of
a close team of experts (instructors) working together.
Ontologies and tutors developed by other teams will not
comply with the M-OBLIGE requirements and will not be
available for cross-personalisation. Since the automatic
ontology mapping techniques have the potential to deal with
the general task of solving domain model discrepancies,
we expect new projects investigating this research direction
to appear.
4.3.3 Resolution of user profile discrepancies
Adaptive systems can collect diverse information about
users and maintain complex user profiles representing
multiple aspects. Even though most of the users’
characteristics modelled by the existing adaptive systems
are typical, the actual profile representation varies from one
system to another. As an example, we consider the structure
of the top categories from two standard learner profiles:
IEEE PAPI (Figure 17) and IMS LIP (Figure 18).
Figure 17 Core categories in the PAPI profile (see online version
for colours)
Source: IEEE-LTSC (2001)
61
Figure 18 Core categories of IMS LIP (see online version
for colours)
Source: IMS (2001)
PAPI’s learner profile has six top-level categories
(IEEE-LTSC, 2001):
•
Personal info: demographic information
•
Relations: learners’ relation with other subjects of
the education process (peers, teachers, etc.)
•
Security: learners’ credentials and access rights
•
Preferences: what objects the learners can and like
to work with
•
Performance: the results of learners’ assessments
•
Portfolio: information about learners’ previous
experiences.
The core categories of IMS LIP are (IMS, 2001):
•
Identification: individual data such as names, addresses,
contact information
•
Goal: personal objectives
•
QCL: qualifications, certifications and licenses
•
Activity: records about education, work and service
(military, community, etc.)
•
Interest: descriptions of the hobbies and other
recreational activities
•
Competency: descriptions of the skills the learner
has acquired
•
Accessibility: cognitive, technical and physical
preferences of the learner
•
Transcript: academic performance of the learner
with respect to a particular institution
•
Affiliation: descriptions of the affiliations associated
with the learner
•
Securitykey: descriptions of the passwords, certificates,
PINs etc.
62
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S. Sosnovsky and D. Dicheva
Relationship: definition of the relations between the
other core data structures, e.g., QCL and the awarding
organisation.
Even for these standardised profiles developed for similar
purposes by large committees of established professionals
from the same research field, the top-level categories greatly
deviate one from another. Identical categories have been
named differently (e.g., PAPI’s personal and IMS LIP’s
identification). Some PAPI categories correspond to several
IMS LIP categories (e.g., PAPI’s relations and IMS LIP’s
affiliation and relationship) and some IMS LIP categories
cover data that belongs to several PAPI categories
(e.g., IMS LIP’s activity and PAPI’s performance and
portfolio). Figure 19 represents the complete mapping of
IEEE PAPI and IMS LIP as given in Klamma et al. (2005).
The discrepancies in user profiles need to be resolved
for meaningful integration of user modelling information.
Even though the list of potential discrepancies in user
profiles will not differ much from the list of domain
discrepancies, in practice, this problem appears to be easier
than the general problem of matching domain ontologies,
because of several reasons. From one side, the development
of a user profile ontology is a more formal task, than the
development of an arbitrary domain ontology; from another,
the set of potential elements is fixed and well-structured,
and the developers of user profile ontologies approach the
design in a more accurate way, than an average domain
ontology author. Besides, the number of existing user
profile ontologies is smaller than the number of domain
ontologies; they are easier to discover, therefore the
majority of developers tend to reuse the work of previous
authors rather than creating completely different versions of
user profile ontologies.
Figure 19 Relationships between the top categories of IEEE PAPI
and IMS LIP (see online version for colours)
Source: Klamma et al. (2005)
Due to these factors, the application of automatic ontology
mapping techniques does not seem feasible for the task of
matching user profile ontologies. Besides, the number of
concepts in user profile ontologies does not exceed
a manually manageable number, and the effect of a
miss-mapping of user profile ontologies would be more
dramatic than it is for mapping domain ontologies (the
entire view of a user profile will be translated incorrectly
comparing to an incorrect model state for a single concept).
Therefore, existing projects exploit one of two main
approaches to the problem of interoperability of user
profiles:
•
development and maintenance of a central user profile
ontology that acts as a common ground for multiple
applications
•
manual mapping of existing user profile ontologies
by setting up conversion rules or build an integrated
model.
The GUMO ontology is an example of the first approach
(see Section 4.1.3). It has been built from scratch for the
purposes of modelling a user in ubiquitous settings. Several
adaptive applications model their users based on GUMO
(Kruppa et al., 2005; Stahl et al., 2007).
As to the second approach, we have previously
discussed several initiatives to merge existing learner
profiles (Dolog and Nejdl, 2003; Jovanovic et al., 2006;
Ounnas et al., 2006) (see Section 4.1.3 for details). The
most cited work (Dolog and Nejdl, 2003) suggests a
representation of learner profiles that inherits categories
from both IEEE PAPI and IMS LIP, and extends them with
new concepts. In Dolog and Nejdl (2007) the authors extend
the previous work with several additional RDF models
(document model, user activity model, etc.) and propose
a unifying approach to the development of adaptive
educational applications on the SW. All declarative and
descriptive knowledge is represented in RDF. The
adaptation strategies are formalised as sets of rules. Among
the benefits of this approach are the high level of
modularity, interoperability, and reusability of existing
models and components. The development of a new system
could be reduced to the development of a new domain
ontology or formulisation of a new set of adaptation rules.
4.3.4 Resolution of scale discrepancies
The final source of divergence between user models is the
different scales used for assessment of users’ attributes.
For example, two educational adaptive systems can use
the overlay user modelling approach, operate in the same
domain, and rely on the same domain ontology, yet they can
model a user’s knowledge using different scales for
knowledge assessment.. Scales can be binary (knows/
does not know), categorical (high/medium/low), numeric
(from 1 to 5), and probabilistic (the certainty that the user
knows the concept). Several problems can arise during the
mapping of two scales:
•
How to map two different categorical scales
(when the category labels are different or the numbers
of categories are different)?
•
How to map a probabilistic scale into a categorical one
(categories thresholds)?
•
How to align the internal inference mechanisms of two
different systems (one system can use an asymptotic
formula for attribute value calculation, when another
can use a Bayesian approach)?
Ontological technologies for user modelling
Scale ontologies could be developed to help solving the first
two problems. If numeric values are expressed using XML
standard data types, they become available for standard
RDF reasoning engines, which can be used to provide
automatic mapping to ontology-based scales. If systems’
internal inference mechanisms are explicated and serialised
in RDF, a set of rules could be developed to map the values
inferred using different algorithms. We are not aware of
existing projects resolving scale discrepancies; further
research has to be done in this direction, since no practical
solution for the problem of user model interoperability is
possible without dealing with scale discrepancies.
Although the resolution of any type of discrepancies will
lead, in practice, to some loss of modelling data, mediated
user models stay a valuable source of information about
users. In most cases, the alternative to a non-precise user
model translated from another system is an empty user
model, which deviates from the actual user’s state much
more drastically.
5
Conclusion
This paper has attempted to bridge two research areas
important for the future generation of web applications: user
modelling and ontologies. Sections 2 and 3 have
summarised the technologies developed in both fields and
Section 4 has detailed the promising trends of how
these technologies have been used together to build
ontology-based adaptive systems. In the first sections,
we have not tried to present the complete lists of existing
approaches. Several cutting-edge user-modelling problems
have not been mentioned, such as user modelling in virtual
reality, privacy-enhanced user modelling, etc. Some topics
of ontological research, while being actively discussed
by the SW community, have been left out of the scope of
this review (such as ontology reasoning and querying,
ontology versioning, ontology validation and evaluation,
ontology robustness and scalability, etc.). Instead of
drawing the complete pictures of these two fields, we have
chosen to describe those technologies that have the potential
to demonstrate the applicability of ontologies for user
modelling in modern AW applications. The choice of topics
covered in the first sections was mainly driven by the
existing projects exploring the “ontology-user modelling”
synergy described in Chapter 4.
The discussed here direction of research is very
dynamic. Most of the works referred in Section 4 have been
implemented in the last five years. Many of the references
report work in progress, which is still under development.
New ideas have been generated and new papers have been
published as we have been writing this overview. While we
could hardly cover every single project applying ontologies
for user modelling and adaptation, we have tried to
mention the most interesting studies from the research
teams that are likely to continue working in this direction.
63
As the field develops, we expect to see not only
new systems implementing more effective and complete
solutions for the problems described in Section 4,
but also new trends addressing novel challenges and
proposing principally different approaches. Some of these
trends, which we believe will attract the attention
of the research community in the nearest future, are given
below.
5.1 Ontology-based adaptive architectures
The role played by ontologies in the adaptive system design,
which is often reduced to the representation of a particular
knowledge component, can be broadened at both the
design-time and run-time. Guarino (1998) proposes several
ways of wider employment of ontologies by information
systems. Architecturally, ontologies can be used to represent
any knowledge component in the system (user model,
domain model, adaptation model, communication model).
This can lead to better reusability and interoperability
of systems’ components, unified design patterns,
and standardisation of communication protocols.
The cross-model knowledge processing in the system can be
entirely based on standard inference engines, which will
make it consistent, effective and transparent for the third
parties willing to reuse the system’s components.
Several research teams are working on the development
of ontology-based adaptive architectures. Personal Reader
is an architecture for adaptive support of reading web
content, based on ontologies and SW services (Henze and
Herrlich, 2004). The Omnibus project is aimed at
developing a unified ontology of learning, instruction,
and instructional design, which provides a framework for
full-scale ontology-based authoring tools for ITSs
(Mizoguchi et al., 2007). The mentioned UbisWorld project
utilises a comprehensive set of ontologies to implement a
framework for interoperable ubiquitous personalisation
(Heckmann, 2006).
5.2 Social semantic adaptive web
A recent trend in the web research focuses on connecting
the SW technologies for representing, retrieving and
processing knowledge with the interactive social interfaces
of Web 2.0. The lack of content in the SW applications and
the lack of structure in the community-authored content of
Web 2.0 call for a cooperative solution. The common
approach of multiple projects originated in this area is to
exploit the large body of raw content and metadata
produced by the users of social web applications, while
employing the structural knowledge and standard
representation technologies offered by the SW. Although
it often comes at the price of formalisation and richness
of the resulting knowledge models, the practicality
of this approach is promising. Many Web 2.0 applications
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S. Sosnovsky and D. Dicheva
merged with SW technologies obtain new functionalities
and/or new ways of use, for example, semantic wikis
(Auer and Lehmann, 2007; Buffa et al., 2008), semantic
community web portals (Gruber, 2008; Staab et al., 2000),
semantically-enhanced tagging facilities (Jaschke et al.,
2008; Newman et al., 2004), etc.
Adaptive technologies have successfully made their
way to the Web 2.0 platform (e.g., collaborative
recommendation (Konstan et al., 1997), social navigation
(Farzan and Brusilovsky, 2006), community-based search
(Smyth et al., 2005)). This review gave multiple examples
of adaptive applications implemented by using SW
approaches. Merging all three paradigms together should
result in an improvement of existing approaches and,
maybe the emergence of new ones. For example, hybrid
recommender systems, implementing both collaborative
and content-based adaptation are known to outperform the
pure collaborative recommendation for a number of tasks.
However, the content-based component is very much
dependant on the quality and richness of content models.
Implementation of such models with the help of ontologies
could improve the content-based adaptive inference, as well
as enhance the population of these models through ontology
mapping, ontology-based annotation and ontology learning
techniques.
Social navigation, while being an efficient technology
for automatic open-corpus content quality control, does not
implement any mechanisms for navigating the individual
user to the best document at the best moment of time.
Semantic technologies can help to ‘personalise’ socially
produced navigational cues through maintaining richer
models of users and content.
One of the biggest challenges for community-based
adaptive search is to identify the right community, which
the user is currently in, in order to tailor the search results
appropriately. Ontologies could help to draw stricter borders
between the communities by enriching their profiles with
semantic information. Currently these profiles are populated
mainly based on the tags and queries used by their
members. Such projects as WordNet and Tag Ontology
(Newman et al., 2004) can help community-based systems
to migrate from lexical to semantic models.
A recently launched project called Twine is a working
example of a joint application of semantic, social and
adaptive technologies (Radar_Networks, 2008). Twine is a
semantic bookmarking service, which allows users to share
information they have discovered on the web, provides
automatic semantic analysis and markup of the shared
web-pages and even generates adaptive recommendations
based on the individual user’s history and resources
submitted by others.
There are more examples of potential synergy between
these areas of web development. We expect more research
and commercial projects to emerge in the nearest future.
Acknowledgement
We would like to express our gratitude to Peter Brusilovsky
and Michael Spring for their help in preparing this review.
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Notes
1
The query has been tossed on 20 August 2009.
Some researchers also use the terms ‘ontology alignment’ and
‘ontology merging’ as synonyms of ‘ontology mapping’.
3
These are not real ontologies used by Amazon.com and
Blockbuster. We have designed them to demonstrate typical
discrepancies that can occur between two models of the same
domain.
2