Automatic Knowledge Exchanging between Tourists via Mobile Devices
Keywords:
Tourism, Ontology Mapping, Semantics, Mobility, Knowledge Discovery, Social Networking,
P2P
ABSTRACT
Purpose
This paper presents an approach for services in the domain of tourism based on a software application in the area
of ontology engineering, showing a methodology for intelligent knowledge based P2P networks creation, in the
tourism knowledge domain, given that, potential tourists share and organize their experiences, interests and
knowledge. Using the proposed software application, they automatically exchange their knowledge, with an
intelligent and transparent way, with other users that have the same or similar interests and make use of it.
Design/Methodology/Approach
The approach followed, was categorizing tourism related interests and services into ontologies (system and user).
Then comparing, using intelligent algorithms them, suggesting, new, unknown to the user, interests. The data was
evaluated by human expert, for providing a guide for correct (according to expert) interestingness of profile
concepts.
Findings
In the paper is presented the outcomes of the software used, running on mobile devices, showing the connection,
among user knowledge profiles and tourism services for them. It has been found that the return results (concepts)
are of high interestingness to the user.
Research Limitations/Implications
Experiments have been performed using one central ontology as reference, with two user ontologies at the same
time. More experimentation, with more users connected concurrently is suggested for further research.
Practical Implications
Of high practical importance is the creation of intelligent social networking processes and user communities based
on interests, for further e-commerce activities, based on a semantic framework.
Originality/Value
The paper fulfils the need for intelligent interaction and distribution of knowledge and content to users in an
autonomous way, anywhere.
1
INTRODUCTION
One of the most significant achievements in the so-called digital era, is the easy access to the international
networks, information channels and access to new ways of digital content. Internet has changed forever the way
that the digital content, cultural heritage and tourism (in all its ways), are interacting among each other to create
new e-commerce opportunities in tourism, that cover personalized needs.
There is a plethora of sites on the Internet that they offer tourism services. These tourism services are
precompiled and the have been developed according to studies to cover larger populations that will be interested in
these. In this work, it is accepted, that personalization in tourism, as in many other domains, is the future in
tourism activities and intelligent techniques. Personalization must be based on new technologies that have to be
developed, to satisfy user (tourist) autonomous needs. To this end, it is proposed an intelligent software
application, that will provide the ability to potential tourists to organize their experience/knowledge obtained after
visiting places of interests and then to automatically interchange it after electronically getting in touch with other
people that have the same or similar interests. The proposed technique is based on the comparison and automatic
interchange of interesting parts of user ontologies. More specifically, is based on exchanged only interesting parts
of ontologies based on structural similarity of ontologies (Hu et. al., 2005) and (Euzenat and Valtchev, 2004) .
The rest of the paper is organized as follows: next section presents the basic ideas and concepts around
relevant technologies, such as intelligent knowledge extraction and distribution, and the kind of problem which
will try to solve. Also, it presents the theoretical background of our approach, the ONARM technique and the
implemented Concept-Net application for tourism, based on the ONARM methodology. Section 3 presents
various use cases and system usage, by presenting user scenarios. Finally, in Section 4 it is presented future work
and concludes with our work, presenting also, the respective list of figures and relevant bibliographical references.
2
ONTOLOGY BASED KNOWLEDGE ENGINEERING
Ontologies are used today and find acceptance in a number of applications like information retrieval (
Pretschner and Gauch, 1999), document management (Lacher and Groh, 2001), agent communication (Huhns and
Singh, 1997), finance (Firat and Madnick, 2001) and e-commerce (Omelayenko, 2001) and (Virtual Vineyards).
Ontology engineering, i.e., designing, developing, maintaining and sharing ontologies, is an emerging
knowledge engineering process. It allows the information organization into taxonomies of concepts, represented
by attributes, and relationships between concepts, represented by IS-A relations, functions, constraints, etc.
Ontologies can be designed and developed by different communities without adopting common standards for
information exchange. Ontologies are imposed by the explosive growth of the Semantic Web, where they are used
to describe the semantics of the data. Due to the decentralized nature of both the WWW and the Semantic Web, it
is inevitable that different communities within the so-called information society represent and treat the same basic
concepts in different ways. For example, the basic concept “Person” is treated entirely differently in a medical
ontology than in a tourism business one.
2.1 THE ONARM TECHNIQUE
Ontology mapping aims at tackling structural and semantic heterogeneity and in-compatibility by determining
correspondences between elements of disparate ontologies. A mapping can be established either directly between
two ontologies (alignment) or indirectly through mapping them onto a third reference ontology which both of them
share as a common upper model (articulation). The work of mapping ontologies is performed mostly by hand,
perhaps supported by a graphical user interface. Of course, performing ontology mapping manually is an
extremely time-consuming and error-prone process. The ontology mapping techniques presented in the literature
are usually based on syntactic and/or semantic heuristics. The latter have been studied in various scientific fields
including machine learning, concept lattices, formal theories, databases and linguistics. In almost all of them user
intervention is required, thus they are semi-automated. Usually, when an automatic decision is not possible, these
techniques suggest possible correspondences, determine conflicts and propose solutions and actions. Then the user
makes the final selection. The proposed methodological framework is based on ONARM ontology mapping
technique (Tatsiopoulos and Boutsinas, 2009a), (Tatsiopoulos et. Al., 2009b) which, given two input ontologies, is
able to map concepts in one ontology onto those in the other, without any user intervention. ONARM, exploits the
structure of the input ontologies, i.e. the concept hierarchies, to determine the mapping.
The ONARM methodology proposes a new model as to discover knowledge from concept based on user profiles.
It assumes an analogy of a concept based world in which we use and therefore we consume concepts according to
our subjective needs and how we anticipate that these are optimally covered by concepts-products. Therefore the
behavior of the knowledge user has to be analyzed and according to this analysis knowledge has to be forwarded to
her/him in an intelligent and transparent way.
The ONARM has been compared to other Ontology Mapping / Aligning techniques (Kalfoglou and Schorlemmer,
2003), concluding its degree of efficiency is very high provided that is among the fewest offering automatic
merging results, without human intervention.
2.2 THE SOFTWARE APPLICATION
The software application that has been developed is called Concept-Net and is composed of two major components
(a) the client application which runs on mobile devices and (b) the server part which processes the personal
ontologies, analyzes, compares, aligns and finally forwards interesting parts of them to online client devices/users.
The content of the specific application is a number of concepts in the area of tourism. The concepts of the domain
are organized in a main reference ontology with concepts – classes of type IS-A in a hierarchical way.
The client part has functionality that allows the user to draw her/his personal ontology in a graph, in the graph
editor. The user is continuously, upon request, being updated from the central server with all the concepts
available and from which selects the ones that are of main interest to him. Afterwards, the user personal ontology
is uploaded to the server and the complete analysis of the ontology is taking place. The user is able, via the
graphical interface to find other online users. Then, automatically, in a dynamic way, is able to update interesting
and relevant parts of her/his ontology, with additional concepts from the reference ontology.
In figure 1, it is presented a part of the tourist domain reference ontology that has been developed for the needs of
testing with real world concepts. The ontology has been implemented in Protégé (The Protégé Ontology Editor,
Stanford University) in RDF/RDF(s) format and then is uploaded to the server for further processing.
2.2.1 System Architecture
(a) Concept-Net has the following three submodules in the client: the “Graphical User Interface”, the “Web
Services Interfaces (WS IFCs)” and the “Operating System Utility” as presented in figure 2. From the above The
“WS IFCs” is the one that transparently connects to the back-end module and provides connectivity with the
respective backend web service.
(b) Concept-Net has the following submodules in the back-end module:
The back-end module (Fig. 6) is currently implemented using the Java Programming Language and is composed
by: the Web Services Interfaces (IFC), the Data Access Layer, the System Logic & Intelligence Module, the
Knowledge Service, the Database and the Operating System & File System Access Layer.
The Web Services Interfaces (IFC) component is responsible for all the communication between the users' client
devices and the back-end system. The Data Access Layer incorporates all the functionality needed to send queries
to system database as well as to receive information from it, during the system operation. This information is
relevant to authentication as well as to user's knowledge profile. The System Logic & Intelligence Module
incorporates all the functionality dealing with the main operation of the proposed methodology. It includes
submodules that implement the following functions: Graph Analysis. This sub-module accepts an ontology in the
form of a graph and performs operations to analyze it, in terms of paths, nodes, leaf nodes, parent and children
nodes of every node. This functionality is achieved using the HP Jena API for ontology querying, in RDF/RDFs
graph formats. Then, the Apriori algorithm for association rule mining is used. This sub-module accepts as input
all the possible paths and outputs only these with the most significant associations among their nodes. Concept
Matching. This sub module, having as input two sets of paths, outputs the most "interesting" ones to match to each
other. Ontology alignment. Based on the above, this sub-module performs the actual implementation and
integration of the concepts into the user's graph.
The “Knowledge Service” component implements a set of function calls so that when it is called properly from the
Web Services Interfaces component, performs the respective task. The function calls implemented in Knowledge
Service are: “Echo”, “GetConfguration”, “LoginUser”, “UpdateInLineStatus”, “GetConceptList”,
“GetUserByStatus”, “UploadUserGraph”, “RegisterUser”, “SynchronizeGraphs” and “GetUserGraph”. The
“Database” stores all the system information, users' knowledge profiles, concepts and so on. Finally, the
“Operating System & File System Access Layer” implements functionality for accessing the OS, so that the
overall system can be easily transferred to a variety of Operating Systems. The Concept Net system has been
implemented for proof of concept purposes, as a full working prototype. It is applied for evaluation purposed to
different domains. Additionally, it has been tested in various structural models for ontologies9. Here, it is applied
to the tourism domain. The objective was to recognize among two Concept Net users (tourists), their personal
ontology graphs, to analyze them using the proposed methodology and finally exchange the concepts that were
found interesting per user.
2.2.2 Graphical User Interfaces
In the following figures, the major parts of the graphical user interface (GUI), that appear on the user’s Personal
Digital Assistant, which runs on Windows Mobile 6.0 operating system, are presented.
Main Menu GUI (Figure 1)
This is the main menu via which the user interacts with the functionality on his device. The main
functionality that is available from here is:
 Login / Register: Functionality to register, authenticate and use the system.
 Join Knowledge Network: The user connects in real time to the system, to interact with other users
that they use it, to exchange tourist – concepts - interests
 Concept Browser: Return all relevant to the user concepts from the server
 Graph Editor: Functionality for the user to draw his concept based profile.
 Settings: Customize settings for connecting to backend server (ip, port, etc.)
GUI for Creation of Personal Profiling (Figure 2)
After registration (first time) and proper user login process, the user will have to create his personal profile
in terms of preferences in relevance with the usage of the device – account and how the system will behave
for the management of his personal interests.
For example, the user might or might not permit the exposition of all of his personal ontologies, while he
maintains a number of them stored in his personal device for the same or for other topics/interests.
This way, system respects and acts according to confidentiality issues, expressed in user’s profiling
mechanism.
GUI for Concept Navigation (Figure 3)
After login, the user is expected to proceed to the part of the application in which:
 He will be able to load a relevant concept list, stored locally in his device.
 He will be able to update via a real time connection with the server, with all concepts that are stored
there.
He will be able, finally to save his personalized list of concepts.
GUI for Representing Personal Ontologies – Graphs (Figure 4)
The user afterwards, has the ability to navigate to the part of the client application that he will create his
personal graph ontology.
This, which is called Graph Editor, permits the user to select concepts from a drop down box and freely
locate them and connect each, node by node, in a hierarchical way, of the type IS-A relation.
For example, our user here is interested in tourist activities that deal with Camping. More specifically, he
would like to the location that he will be suggested to select for camping, to exist some type of Sports
infrastructures and especially, if possible, Volley (infrastructures, local games, small championships, beach
volley and so on). He would also prefer the camping location, to be more close to a quite location than to a
very crowded and busy one, since he plans his vacation for Relaxation purposes.
What it happens next is that, while the user connects to the system, his personal ontology – graph is getting
analyzed and is recognized that the system deals with a “sporty” type of individual, providing to him a new
to him concept Sailing, as additional knowledge to his profile.
Then, the system adds this to his personal ontology (after user acceptance).
3 USE CASES – USAGE
In order to evaluate the proposed methodological framework, Concept Net has been tested under different
scenarios.
For this, a reference ontology has been developed, (see Figure 5), while we asked users to develop their personal
ontologies. In the following, we present such user case scenarios.
As an example of a use case scenario, the user selects from the populated list of concepts the ones that seeks
knowledge about. For example in our case a user A, selects as a main concept the concept of “camping” looking
forward for vacations in a camping. In his personal mobile device, in the graph editor tool, associates it directly
with the concept “relaxation”, meaning that the user seeks a place for camping that it exists in a more quite place
than a cosmopolitan and busy one. Additionally, the user is interesting in having sports activities infrastructures
around it, so the user creates the node “sports” under the node “camping”. The user repeats the process, creating
her/his personal graph of interests and then the user interacts with another online Concept Net user B of the P2P
Knowledge Network or the system’s main reference ontology of tourism concepts. What happens is that the
system after analyzing the two personal ontologies, pushes automatically the concept “sailing” that exists on user’s
B personal ontology, as relevant to interests of user A. User A accepts the automatically transferred concept(s) –or
part of them- and modifies her/his personal ontology graph in the mobile device. Thus, all the users of the system
are connected resulting in a P2P network, using the ability of the communication channels and techniques of the
internet to create a socially based dynamic network for tourism knowledge extraction and distribution system.
The system is also able, based on the concepts that exist on his personal tourist ontology (profile), to present in
dynamic way marketing information about the above, in the form of advertisement banners, on the top of the
graphical interface of his device, for e-commerce exploitation purposes.
A second use case scenario is relevant to the tourism activities (visits, tours, overnight staying elsewhere, except
the primary place, change of scheduling, etc) and cultural activities. Culture includes areas, such, history,
photography, various types of theatre and singing, organization of special events and happenings, cultural tours
and so on. In our use case scenario, our virtual tourist A, plans a visit to Greece and her/his primary objective is to
“visit places, that there exist ‘ancient’ findings”. Additionally, if possible, to find in a complementary way, other
activities that correlates to its primary objective. She/He plans to stay mainly in Athens. How our system will
assist her/him?
The problem here, for non human inference, becomes the understanding first of all, of the specific relevant
terminology. For example, what kind of ‘ancient’ finding is the tourist interested in? Are preferable in her/his
profile Hellenistic, Classical or Byzantine findings? Does he/she mean by ‘finding’ only ruins of some specific
type (temples, churches, sacred places, etc) or also includes relevant material like sculpture and painting that are
kept in museums? To provide accurate service to the potential tourist, via an automated and personalized way,
Concept-Net, will analyze with an intelligent way and according to its personal profile ontology her/his real
personal needs. Given for example, that our tourist is a professor of classical Archaeology and provided that
she/he is connected with a majority of people interested in classical archaeology too, Concept-Net, understands
and infers, that in her/his limited time will prefer to visit a number of places that exist in a certain area of Greece
(e.g. Delphi), that has one night to stay in place around and will visit in a tour similar places in the area.
Therefore, the system, will present on his personal mobile device advertisement of major hotels in the area and
tour organizers as well as places that will have material for sale (relevant books for example) relevant to his topics
of interest.
As a third complementary scenario to the above, lets assume that the user after visiting Greece returns to her/his
country at some point of time. Then having stored in the mobile device all the above information as part of her/his
personal ontology, while the user will be in the cafeteria of the University that the user teaches, her/his mobile
device (under his permission) interacts with other mobile devices that exist in short range (or their users are online
to the system) and intelligently transmits knowledge to another user that has the same interest (at some point of
time to visit a Mediterranean country that has ‘ancient’ findings). Interacting this way, the user becomes and
establishes a member of a social dynamic and intelligent knowledge network for activities around the thematic
area of cultural tourist activities.
4. CONCLUSIONS - FUTURE WORK
Concluding, new information technology techniques have been examined in this work (mobility, intelligent
knowledge extraction, ontologies), based on semantics and specifically semantically enabled web applications, will
influence the interaction between tourists and services that are targeted for them. Also, a new way, that this
semantic application will provide a means for relative users to create knowledge based social networks, in the area
of tourism. It has been presented also, a software application (Concept-Net) which is based on ONARM
methodology for exchanging interesting parts of user profiles in the knowledge domain of tourism. Along with
this, it has been presented an intelligent way for performing e-commerce activities in the same domain of tourism,
which is based on a much targeted way for advertisement.
Currently, the application has been tested and is working on PDA’s and Smartphones that run the Windows
Mobile 6.0 operating system. It is expected to extend the application adaptation, into I-Phone and Symbian
operating systems, so to reach as much as possible number of potential Concept-Net users.
It is also planned to optimize all internal methodological components so that to optimize the overall system
functionality. More specifically, to develop and publish the WEB services as a protocol for easy integration with
third party applications, in a relevant UDDI server. This way, Concept-Net application will be open and based on
an acceptable WEB service protocol being in a position to interact with other, semantic enabled applications, in the
area of tourism, available in a specific URI.
5. LIST OF FIGURES
5.1 FIGURE TITLES
Figure 1 – Main Menu
Figure 2 – GUI for Creation of Personal Profiling
Figure 3 – GUI for Concept Navigation
Figure 4 – GUI for Representing Personal Ontologies
Figure 5 – Reference Ontology (part)
Figure 6 – Concept Net Back End System Architecture
5.2 FIGURES
Figure 1
Figure 3
Figure 2
Figure 4
Figure 5
Figure 6
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The Protégé Ontology Editor, http://protege.stanford.edu/
Virtual Vineyards, http://www.virtual-vineyards.org