Table Of ContentSpirosSirmakessis(Ed.)
AdaptiveandPersonalizedSemanticWeb
StudiesinComputationalIntelligence,Volume 14
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Vol.14.SpirosSirmakessis(Ed.)
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AdaptiveandPersonalizedSemanticWeb,
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2006
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FoundationsofDataMiningandKnowledge
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MachineLearningandRobotPerception,
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Spiros Sirmakessis
(Ed.)
Adaptive and Personalized
Semantic Web
ABC
Dr.SpirosSirmakessis
ResearchAcademic
ComputerTechnologyInstitute
RigaFereou61
26221Patras
Greece
E-mail:[email protected]
LibraryofCongressControlNumber:2005937510
ISSNprintedition:1860-949X
ISSNelectronicedition:1860-9503
ISBN-10 3-540-30605-6SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-540-30605-4SpringerBerlinHeidelbergNewYork
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Foreword
Web Personalization can be defined as any set of actions that can tailor the
Webexperiencetoaparticularuserorsetofusers.Toachieveeffectiveperson-
alization,organizationsmustrelyonallavailabledata,includingtheusageand
click-stream data (reflecting user behaviour), the site content, the site struc-
ture,domainknowledge,aswellasuserdemographicsandprofiles.Inaddition,
efficientandintelligenttechniquesareneededtominethisdataforactionable
knowledge, and to effectively use the discovered knowledge to enhance the
users’ Web experience. These techniques must address important challenges
emanating from the size and the heterogeneous nature of the data itself, as
wellasthedynamicnatureofuserinteractionswiththeWeb.Thesechallenges
include the scalability of the personalization solutions, data integration, and
successful integration of techniques from machine learning, information re-
trievalandfiltering,databases,agentarchitectures,knowledgerepresentation,
data mining, text mining, statistics, user modelling and human-computer in-
teraction.TheSemanticWebaddsonemoredimensiontothis.Theworkshop
will focus on the semantic web approach to personalization and adaptation.
TheWebhasbeenformedtobeanintegralpartofnumerousapplications
inwhichauserinteractswithaserviceprovider,productsellers,governmental
organisations, friends and colleagues. Content and services are available at
different sources and places. Hence, Web applications need to combine all
availableknowledgeinordertoformpersonalized,user-friendly,andbusiness-
optimal services.
The aim of the International Workshop on Adaptive and Personalized Se-
mantic Web that was held in the Sixteenth ACM Conference on Hypertext
and Hypermedia (September 6-9, 2005, Salzburg, Austria) was to bring to-
gether researchers and practitioners in the fields of web engineering, adaptive
hypermedia,semantic web technologies, knowledge management, information
retrieval,usermodelling,andotherrelateddisciplineswhichprovideenabling
technologies for personalization and adaptation on the World Wide Web.
VI Foreword
Topics of the Workshop include but are not limited to:
• design, methodologies and architectures of adaptable and adaptive Web
information systems
• user interface design for adaptive applications on the Web
• semantic web techniques for adaptation
• authoring of adaptive hypermedia for the Web
• distributed user modelling and adaptation
• semantic web mining
• personalized taxonomies or ontologies
• hybrid recommendation systems
• model integration for personalization and recommendation systems
• web usage, content, and structure mining
• automated techniques for generation and updating of user profiles
• machine learning techniques for information extraction and integration
• applications of relational data mining in personalization
• adaptive personalized web applications
This workshop could not have been held without the outstanding efforts of
Marios Katsis at the workshop support. I would like to thank the programme
committeemembersfortheireffortsandsupport.Finally,recognitionandac-
knowledgementisduetoallmembersoftheInternetandMultimediaResearch
Unit at Research Academic Computer Technology Institute.
Dr Spiros Sirmakessis
Assistant Professor
R.A. Computer Technology Institute
[email protected]
Programme Committee and Reviewers
Bamshad Mobasher, School of Computer Science, Telecommunication, and
Information Systems, DePaul University, USA.
Olfa Nasraoui, Dept of Computer Engineering & Computer Science, the
University of Louisville, USA.
Lars Schmidt-Thieme, Computer-based New Media, Institute for Com-
puter Science, University of Freiburg, Germany
Martin Rajman, Center for Global Computing, EPFL, Swiss Federal In-
stitute of Technology, Switzerland.
Ronen Feldman, Department of Computer Science, Bar-Ilan University,
Israel.
SpirosSirmakessis,ComputerTechnologyInstituteandTechnologicalEd-
ucational Institution of Messolongi, Greece.
Peter Haase, Institute AIFB, University of Karlsruhe, Germany.
Foreword VII
Michalis Vazirgiannis, Department of Informatics, Athens University of
Economics & Business, Greece.
Ioannis Hatzilygeroudis, Computer Engineering and Infomatics Depart-
ment, University of Patras and Computer Technology Institute, Greece.
Steven Willmott, Languages and Systems Department, Universitat Po-
litecnica de Catalunya, Spain.
Michalis Xenos, Hellenic Open University, Greece.
MiltiadisLytras,ComputerEngineeringandInfomaticsDepartment,Uni-
versity of Patras and Computer Technology Institute, Greece.
Contents
An Algorithmic Framework for Adaptive Web Content
C. Makris, Y. Panagis, E. Sakkopoulos, A. Tsakalidis ................ 1
1 Introduction ................................................. 1
2 Background and Related Work................................. 2
3 An Overview of Metrics for Webpages and Site Subgraphs ......... 3
3.1 Access Smells: Absolute, Relative, Spatial and Routed Kinds .. 3
3.2 Recording User Visits and Hot Subgraphs................... 4
4 AlgorithmsforOrganizingWebContentAccordingtoMinedAccess
Patterns .................................................... 7
4.1 Some Preprocessing Steps................................. 7
4.2 Offline Optimal Reorganization Algorithm .................. 7
4.3 Online Personalization Using Adaptive Data Structures....... 8
5 Conclusions and Future Steps.................................. 9
References ...................................................... 10
A Multi-Layered and Multi-Faceted Framework for Mining
Evolving Web Clickstreams
O. Nasraoui..................................................... 11
1 Introduction ................................................. 11
2 Related Work................................................ 14
2.1 Mining Evolving Data Streams ............................ 14
2.2 Mining Evolving Web Clickstreams ........................ 16
3 Proposed Framework ......................................... 18
3.1 Mining Evolving Data Streams ............................ 18
3.2 Mining Evolving Web Clickstreams and Non-Stop,
Self-Adaptive Personalization.............................. 25
3.3 Integrating Multiple Sources for Information Need Assessment. 27
3.4 Putting the Icing on the Cake: Higher-Level Web Analytics ... 28
3.5 Hybrid Recommender System ............................. 31
4 Conclusion .................................................. 31
References ...................................................... 32
X Contents
Model Cloning: A Push to Reuse or a Disaster?
M. Rigou, S. Sirmakessis, G. Tzimas............................... 37
1 Introduction ................................................. 37
2 Field Background and the Notion of Model Cloning............... 39
3 WebML: A Brief Overview .................................... 41
4 A Methodology for Mining Model Clones ........................ 42
4.1 Conceptual Schema Transformation ........................ 42
4.2 Potential Model Clones Selection .......................... 44
4.3 Potential Model Clones Categorization ..................... 44
5 Metrics and Quality Evaluation ................................ 46
5.1 Consistent Use of Model Clones ........................... 46
5.2 Similarity of Model Clones ................................ 47
5.3 Evaluation of the Link Topology........................... 49
6 Refactoring Opportunities ..................................... 50
7 Conclusions & Future Work ................................... 51
References ...................................................... 53
Behavioral Patterns in Hypermedia Systems: A Short Study
of E-commerce vs. E-learning Practices
A. Stefani, B. Vassiliadis, M. Xenos................................ 57
1 Introduction ................................................. 57
2 Adaptive Hypermedia Systems: State of the Art.................. 58
3 Adaptation Requirements: E-learning vs. E-commerce............. 60
4 Conclusions.................................................. 62
References ...................................................... 63
Adaptive Personal Information Environment Based
on Semantic Web
T. Maneewatthana, G. Wills, W. Hall .............................. 65
1 Introduction ................................................. 65
2 Background ................................................. 66
3 System Overview............................................. 67
3.1 Data Creation, Storage and Adaptation .................... 68
3.2 Sharing, Reusing and Enriching the Information ............. 70
3.3 Implementation.......................................... 72
4 Related Work................................................ 72
5 Summary.................................................... 73
References ...................................................... 73
A Multilayer Ontology Scheme for Integrated Searching
in Distributed Hypermedia
C. Alexakos, B. Vassiliadis, K. Votis, S. Likothanassis................ 75
1 Introduction ................................................. 75
2 The Multilayer Ontology Scheme ............................... 77
3 Architectural Issues of the Proposed Solution .................... 80
4 Conclusions.................................................. 81
References ...................................................... 82
Contents XI
htmlButler – Wrapper Usability Enhancement
through Ontology Sharing and Large Scale Cooperation
C. Schindler, P. Arya, A. Rath, W. Slany........................... 85
1 Introduction ................................................. 85
2 Previous Technology.......................................... 86
3 htmlButler .................................................. 86
3.1 No Installation .......................................... 87
3.2 Easy Configuration and Usability .......................... 87
3.3 Resistance and Maintenance .............................. 87
3.4 Appropriate User Levels .................................. 87
3.5 htmlButler in Action ..................................... 88
4 Semantic Supported Wrapper Generation........................ 88
5 Shared Ontologies ............................................ 89
6 htmlButler Status Quo........................................ 90
7 Conclusion .................................................. 91
References ...................................................... 93
A Methodology for Conducting Knowledge Discovery
on the Semantic Web
Dimitrios A. Koutsomitropoulos, Markos F. Fragakis, Theodoros
S. Papatheodorou ................................................ 95
1 Introduction ................................................. 95
2 Approaches for Reasoning on the Web .......................... 96
3 An Inference Methodology for the Semantic Web ................. 97
3.1 Choosing the Appropriate Formalism....................... 97
3.2 Suitability for the Web ................................... 98
3.3 Choosing a Reasoning Back-End........................... 99
4 The Knowledge Discovery Interface .............................100
4.1 General Description and Architecture......................100
4.2 Results .................................................101
5 Conclusions..................................................104
References ......................................................104
