Fuzziness in the Semantic Web: Survey and Future Directions
Seyed Koosha Golmohammadi, Marek Reformat, and Witold Pedrycz
Department of Electrical and Computer Engineering, University of Alberta, Canada
{koosha, reform, pedrycz}@ece.ualberta.ca
Abstract
The Web that we use every day includes billions of
web pages and is rapidly growing. Therefore
extraction of relevant information from the web is not
trivial. Providing web services and improving
man/machine interoperability are important issues that
should be satisfied even in the presence of incomplete
and inconsistent information. This paper reviews
current results of research on representing uncertainty
and approximate reasoning in the web environment.
We also examine methodologies that can address
situations that involve uncertainty. We focus on fuzzy
methods.
1. Introduction
World Wide Web Consortium (W3C), founded in
1994, is an international consortium working on
development of Web standards and guidelines that
address many critical aspects of the Web. For example,
information exchange among web applications, better
utilization of web technologies, as well as interaction
between humans and computers. In a nutshell, W3C’s
mission is to make Web as useful as possible to as
many users as possible.
In 2001, the inventor of the Web and director of
W3C Tim Berners-Lee introduced a new vision of the
World Wide Web that is called the Semantic Web. He
envisioned an environment where software agents are
capable of analyzing the web contents and performing
many tasks on user behalf at different levels of
difficulty. The most important novelty of the Semantic
Web is application of ontology as a means for effective
integration and sharing of information - “people can’t
share knowledge if they do not speak a common
language” [10].
Ontologies are widely discussed in Artificial
Intelligence and have a long history in philosophy.
They support knowledge sharing through well defined
and partially ordered descriptions of concepts.
Ontology
is
an
“explicit
specification
of
conceptualization” [17]. In other words, ontology is a
formal description of categories (concepts), their
properties (known as slots) representing various
features and attributes, and restrictions imposed on the
slots. The combination of ontology and a set of
individuals (instances of categories) constitute a
knowledge base.
At its fundamentals ontology expresses well-defined
definitions of concepts and relationships among them.
All this is done with an assumption that all the
knowledge is precise and accurate. However, it is
highly possible that in the freely growing Web such
assumption is not always valid. Therefore, the concepts
of uncertainty in knowledge representation and
reasoning with uncertainty become very important
components influencing further growth of the Web.
In this paper we review the current state of
utilization of concepts of uncertainty and approximate
reasoning in the Web environment. This includes
methods designed for representing and reasoning with
knowledge when Boolean yes/no values are
inapplicable. There are different approaches applied to
situations that involve uncertainty such as fuzzy sets,
probability theory, belief functions, rough sets and
random sets. The most commonly used approaches to
deal with uncertainty in the Web are Bayesian models
[12, 13, 21] and fuzzy logic [ref]. In this paper we
focus on fuzzy approaches. There are two objectives of
this paper:
1. represent web utilization situations that would
benefit from the application of uncertainty and
approximate reasoning;
2. review methodologies that can be applied to these
situations focusing on fuzzy approaches.
An extensive study of related works has been
performed to address the above objectives. We hope
the result of this research will bring better
understanding of the concept of uncertainty in the web
environment and the need for its inclusion in
development of new web technologies. We also stress
that there is a need for a standard representation of
uncertainty in the web environment. Currently, there
are no web standards addressing the issues of
representing uncertainty and reasoning with
uncertainty.
The rest of this paper is structured as follows.
Section 2 discusses the issue of uncertainty in the
context of the Web. Section 3 reviews the technologies
and definitions employed in this paper: uncertainty
representation, principles of fuzzy theory, and a brief
description of the Semantic Web. Section 4 examines
approaches used for representing knowledge based on
the concept of fuzziness. Section 5 discusses different
applications of fuzzy methods to handle uncertainty in
the semantic web framework, and finally Section 6
concludes the paper with a discussion.
2. Uncertainty and Web Utilization
2.1 Sources and Nature of Uncertainty
The Web is consisted of immense amount of data.
Information retrieval from this extremely huge source
is not immune to inconsistencies or uncertainties.
Uncertainty or imprecision on the web can be related to
two main factors: first, even in extremely accurate
measurements we are uncertain about the implications;
and second, the human perception [44] is
fundamentally unable to conduct completely accurate
measurements.
The Uncertainty Reasoning for the World Wide
Web Incubator Group (URW3 XG) created under the
W3C is dedicated to define reasoning and
representation of uncertainty on the web and related
technologies more appropriately. URW3 considers two
facets of uncertainty:
1. aleatory: uncertainty is an inherent property of the
world;
2. epistemic: uncertainty is due to someone’s lack of
knowledge.
In the first case we can assign degrees of truth and in
the second we might assign different possibility
degrees to possible alternatives. Furthermore, we can
consider five types of uncertainties that may occur on
the web: inconsistency, ambiguity, vagueness,
randomness, and incompleteness. Examples of the
above uncertainty types on the web scale are discussed
in the next section.
2.2 Example Scenarios
Multiple aspects of the Web can be associated with
uncertainty. The following example scenarios are just a
few that represent the most intuitive illustration of
needs that uncertainty can address.
 Information correctness and availability – it is the
essence of the Web, and such issues as partially
correct or even incorrect information or lack of
information have to be addressed. Representation
and reasoning with uncertainty provide ways for
drawing conclusions and making decision in such
circumstances.
 Information precision – information acquired by a
user can be inherently imprecise. For example,
weather forecast. Standards for representing and
reasoning with uncertainty enable utilization of
such information.
 Concept mapping between ontologies – the
Semantic Web vision is based on ontologies and
interaction among them. The issue of expressing
degrees of similarity between concepts is related to
vagueness and ambiguity.
 Identification and Composition of Web Services –
any activities related to identification of services
requested by a user and building a complex
services based on a simple ones have to be
equipped with methods and techniques that
address the problem of imperfect match between
user’s request and available services.
3. Preliminaries
3.1 Uncertainty Representation
3.1.1 General Approach
A number of different approaches for representing
uncertainty can be found in [24]. Below we shortly
review descriptions of the most intuitive ones:
 Probability theory: Uncertainty means assigning a
number between 0 and 1 to subsets of alternatives.
This number – probability – represents the
likelihood that the desired alternative is in a
subset.
 Fuzzy set and fuzzy measure theories: Fuzzy sets
are capable of expressing imprecision and
vagueness. In fuzzy sets, our focus is not on a
matter of affirmation or denial, but rather on a
matter of degree. A number of special classes of
measures are used: plausibility and belief
measures, as well as the classical probability
measures. Fuzzy measures can indicate levels of
information sufficiency to determine if an element
belongs to a specific set.
 Rough set theory: Uncertainty about an element
belonging to a set is expressed in terms of two
subsets, a lower approximation and an upper
approximation.
Among these three approaches we are going to focus
on fuzzy-based approach [9, 14, 25, 38]. There is a
fundamental difference in the semantics of fuzzy logic
and probabilistic logic. In fuzzy logic, a statement can
be true to a certain extent or an entity belongs to a class
to a certain degree. This degree is assumed to be
known with certainty. In probabilistic reasoning, there
is a probability that a statement is true or false. In this
case the statement itself is either true or false, but
neither both nor something in between. Hence fuzzy
logic sees the world as continuous instead of binary,
while probabilistic logics make a claim about the
randomness of the world or the observer’s state of
certainty [36].
3.1.2 Principles of Fuzziness
Real situations are very often not crisp and
deterministic therefore they cannot be described
precisely. They are very often uncertain or vague in a
number of ways. One aspect of uncertainty is related to
lack of information about the future state of the system.
This type of uncertainty is handled appropriately by
probability theory and statistics. It is assumed that the
events are well defined. This is in contrast to the
vagueness concerning the description of the semantic
meaning of the event, phenomena or statements, which
is called fuzziness [30, 41, 45].
Fuzziness can be found in many areas of daily life.
It is particularly frequent, however, in all areas in
which human judgment, evaluation, and decisions are
important. One of the most important reasons for that is
that human daily communication uses natural languages
and a good part of human thinking is done with it. In
these natural languages the meaning of the words is
very often vague. The meaning of a word might even
be well defined, but when using the word as a label for
a set, the boundaries within which objects belong to the
set or not, become fuzzy or vague. Examples are words
such as “birds” (how about penguins, bats, etc.?), “red
flowers”, but also terms such as “tall men”,
“creditworthy customer”. In this context, two kinds of
fuzziness with respect to their origins can be
distinguished: intrinsic fuzziness and informational
fuzziness. The former is illustrated by “tall men”. This
term is fuzzy because the meaning of tall is fuzzy and
dependent on the context. An example of the latter is
the term “creditworthy customer”: a creditworthy
customer can possibly be described completely and
crisply if a large number of descriptors are used.
However, this is more than a human being could handle
simultaneously. Therefore the term, which in
psychology is called a “subjective category” becomes
fuzzy.
The idea of fuzzy theory was first introduced by
Lotfi Zadeh at the University of California at Berkeley
in the 1960s [42]. Zadeh was working on the problem
of computer understanding of natural language. Natural
language is not easily translated into the absolute terms
of “true” and “false”. Fuzzy logic includes “true” and
“false” as extreme cases of truth about phenomena or
statement. Fuzzy logic also includes the various states
of truth in between. For example, the result of a
comparison between two things could be not “tall” or
“short” but “0.38 of tallness”.
3.2 Semantic Web and Ontology
The concept of the Semantic Web was introduced in
May 2001 in Scientific American by Tim Berners-Lee,
James Hendler, and Ora Lassila [6]. Over the last years
the Semantic Web has been described in many ways:
an extension of the current web in which information is
given well-defined meaning, a place where machines
can analyze all the data on the Web [6]. A common
element of all of these definitions is a reference to a
new method of representing data. The formation of the
Semantic Web has been led by advances in the area of
data and knowledge representation.
In a nutshell, the Semantic Web can be seen as a
new representation of resources on the World Wide
Web. It is virtually a hub of linked information that can
be accessible and operable by programs. These
programs can be in a form of software agents or any
other applications which are capable of handing the
semantics of the information.
The new representation of resources on the web is
based on usage of ontology. Ontology is a formal,
explicit specification of a shared conceptualization
[18]. It is a set of well-defined classes to describe data
models in the specific domain. Ontology has ability to
present interrelated resources. Together with their
instances, ontologies work as knowledge characters to
express the individual facts [31].
In the Semantic Web environment ontology is
specified using Resource Description Framework
(RDF). RDF is a foundation for processing metadata
[32]. RDF is a standard for describing resources and
information on the web. It provides interoperability
between applications that exchange machineunderstandable information on the Web. Resource
Description Framework Schema (RDFS) is used as an
ontology language supporting exchange of knowledge
over the web. RDF and RDFS serve as the basic
methodology of expressing web resources in the form
of triples: a subject, a predicate (i.e. verb), and an
object (consider them as start, label and end of the edge
respectively in a labeled, directed graph). Another
ontology specification language is a combination of
DARPA Agent Markup Language (DAML) and
Ontology Inference Layer (OIL) called DAML+OIL. It
enables the creation of ontologies for any domain and
the instantiation of these ontologies in the description
of specific web sites. DAML+OIL enhances and
extends RDFS with richer modeling primitives [37] to
represent the semantics of resources and information.
The latest web resource ontology language is Web
Ontology Language (OWL), which has been proposed
as the recommendation by W3C. OWL has many
correspondences with Description Logic (DL). DLs [4]
are considered the most important formalism to
represent knowledge of an application domain. They
combine traditions of Frame-based systems, Semantic
Networks and KL-ONE-like languages, ObjectOriented representations, Semantic data models, and
Type systems. OWL is not only for representing
information on the web, but it also improves the
capability to process the information and increases the
interoperability among software agents [26]. OWL
defines a family of three languages: OWL Lite, OWL
Full, and OWL-DL.
4. Fuzziness and Ontology Languages
The developments related to Semantic Web, and
especially the application of ontology to knowledge
representation, have created a suitable setting for
representing uncertainty.
Fuzzy OWL, developed in the National Technical
University of Athens [33], has been proposed in 2006.
In this approach, a class is defined by a membership
function that returns the membership value between
[0,1] representing a degree of belonging of a given
object to the class. Fuzzy OWL uses crisp OWL’s
syntax for class, property axioms and definitions.
Reasoning is done using a reasoning platform – Fuzzy
Reasoning Engine (FiRE), and FiRE uses RACER DL1
engine syntax. Fuzzy of OWL (FOWL) [46] is another
extention to the OWL by fuzzy logic to capture
uncertain and imprecise knowledge with modifying
operators.
DL in the web ontology language (OWL-DL)
corresponds to SHOIN(D)2 description logic. In other
words OWL-DL is using SHOIN description logic to
represent knowledge and reason about it. Straccia
presented a fuzzy extension of SHOIN(D) showing that
its representation and reasoning capabilities go beyond
classical SHOIN(D) [34]. A main feature of fuzzy
SHOIN(D) is that the subsumption relation between
classes and the entailment relation is no more a crisp
yes/no problem, but it becomes now fuzzy, i.e. is
established to some degree. Since many languages such
as OWL-DL are based on DLs therefore a better
understanding of DLs is indispensable for Semantic
Web researchers [22].
In addition, research is being conducted in the area
of introducing rules to OWL. Semantic Web Rule
Language (SWRL) is a proposal that combines OWL
(DL and Lite) with the Rule Markup Language
(RuleML). Fuzzy-SWRL (f-SWRL) is a fuzzy
extension of Semantic Web Rule Language [28]. In
both the antecedent and consequent of f-SWRL rules
atoms can have weights between [0,1]. f-SWRL
provides a powerful and flexible knowledge
representation and very convenient for multimedia
domain.
The results of work on fuzzy ontologies are reported
in [35]. A framework called Fuzzy Ontology
Generation frAmefork (FOGA) has been developed. It
combines fuzzy logic and Formal Concept Analysis
(FCA) [15] to represent the uncertainty information by
a value in the range from 0 to 1 (linguistic variables are
no longer needed). FOGA automatically generates
fuzzy ontologies based on data with uncertainty.
5. Fuzziness in the Semantic Web Systems
First steps in introduction of fuzziness to knowledge
representation are associated with first applications of
fuzziness to building web applications.
A collaborative filtering multi-agent model was
introduced in [19]. It relies on fuzzy linguistic
approach [43]. The retrieval capabilities of this model
do not utilize a user’s profile what is seen as a
drawback. This limitation is addressed in the further
work [20] through modifying the model by
incorporating a user profile to improve information
retrieval. The new model combines semantic web
technologies with a dynamic user’s profile relying on
fuzzy linguistic techniques.
2
1
www.sts.tu-harburg.de/~r.f.moeller/racer
SHOIN(D) forms the core of OWL-DL (OWL-DL is a syntactic
variant of SHOIN(D))
Haibin and Yan proposed a framework called soft
Semantic Web Services agent (soft SWS agent) [40]
providing high quality semantic web services using
fuzzy neural networks and genetic algorithms. The core
of soft SWS agent is the Intelligent Inference Engine
(IIE) that uses a four-layer fuzzy neural network.
Linguistic variables entered to the network are
transformed into output variables after undergoing
fuzzy processing.
A concept-matching information retrieval system
that is capable of “retrieving web pages that are
conceptually related to the implicit concepts of the
query” is introduced in [16]. The system uses fuzzy
synonymy and fuzzy generality interrelations as a
means of representing word interrelations. It applies
Synonymy-Based Concept Representation Model (FISCRM) to extract the concepts from web pages and
user’s queries. The vectors used in FIS-CRM are fuzzy
values representing occurrences of concepts instead of
terms.
Acampora and Loia describe a multilayer
architecture to design Ambient Intelligent (AmI) [5]
systems providing efficient and uniform utilization of
control activities [1]. This multiplayer architecture
employs markup-based technologies to transform rough
information on sensors, actuators and services towards
“smart data”. In particular they are using Fuzzy
Markup Language (FML) [2, 3] to provide fuzzy web
services. FML language is a novel computer language
used to model control systems based on fuzzy logic
theories. The main feature of FML is the transparency
property: the FML programs can be executed on
different hardware without additional efforts. This
property is fundamental in ubiquitous computing
environment where computers are available throughout
the physical environment and appear invisible and
transparent to the user.
Nikravesh introduces a new architecture for
semantic web search engines based on Fuzzy
Conceptual Model (FCM) to handle the ambiguity and
imprecision of the concept on the Internet [27]. In the
FCM approach, the concept is defined by a series of
keywords with different weights depending on the
importance of each keyword. Ambiguity of concepts is
defined by a set of imprecise concepts described using
fuzzy concepts. The fuzzy concepts are related to a set
of imprecise words identified by context. Imprecise
words can be translated into precise words using
ontology and ambiguity resolution through clarification
dialog.
A popular statement about the Web – “anyone can
say anything about anything” means that information
can be of different trustworthiness. The agents in the
semantic web framework have to be able to make
judgments to choose a single, most reliable source from
alternative sources of information. Trust is an essential
component of the semantic web vision [6-8].
In [11], the authors treat trust as a degree that a
source can be trusted. They introduce a model that
takes into account partial trust, distrust and ignorance
simultaneously. This model is particularly useful when
the trustworthiness of many sources of information is
unknown for a user at the beginning. This does not
mean the user distrust all sources but eventually further
evidence reveals their credibility.
6. Discussion
6.1 Knowledge Representation
Currently, typical ontology formalisms have very
limited or no capabilities to represent different aspects
of uncertainty. Uncertainty is inherently present in
many application domains. This has initiated research
activities leading to additions of elements of
probabilistic and fuzzy theories to existing knowledge
representation formats. As we presented above,
fuzziness has already been introduced to ontologies.
Some of the items that sill need attention are:
 extensions to OWL-DL leading to increased
expressiveness of fuzzy DL;
 construction of fuzzy ontologies where
relationships among concepts are expressed by a
number in the range from 0 to 1 [35], development
of methods for automatic construction of such
ontolgies and their interaction with normal (crisp)
ontologies;
 development of fuzzy-based methods and
algorithms for matching and comparison of
ontologies.
It should be also stated, that fuzzy logic cannot
address all faces of imperfect knowledge. For example,
rough sets theory [29] has been proposed to deal with
indiscernibility of objects. Therefore, fuzzy methods
used to represent ontologies can be combined with
rough sets to handle uncertainty in DLs [23].
6.2 Web Services
The Semantic Web promises a change in a way a
human will use the Internet. According to its motto web
agents should be able to act on behalf of users and like
users. It seems that fulfillment of that promise means
existence of agents that have capabilities to deal with
uncertainty. It is essential to develop agents that can
use imprecise information and reason about it. With the
assumption that information uncertainty can be
expressed by ontology, there is a need for methods and
techniques able to automatically identify levels of
information uncertainty, store that information, and
reason about it. Utilization of all those things depends
on existence of open-source and commercial reasoning
engines capable of handling uncertainty.
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