Cordeiro / Filipe Web Information Systems and Technologies

5th International Conference, WEBIST 2009, Lisbon, Portugal, March 23-26, 2009, Revised Selected Papers
1. Auflage 2010
ISBN: 978-3-642-12436-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 45, 310 Seiten

Reihe: Lecture Notes in Business Information Processing

This book contains the thoroughly refereed and revised best papers from the 5th International Conference on Web Information Systems and Technologies, WEBIST 2009, held in Lisbon, Portugal, in March 2009, organized by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), in collaboration with ACM SIGMIS and co-sponsored by the Workflow Management Coalition (WFMC). The 22 papers presented in this book were carefully reviewed and selected from 203 submissions. The papers are grouped in four parts on Internet Technology, Web Interfaces and Applications, Society, e-Business, and e-Government, and Web Intelligence.

Autoren/Hrsg.

Cordeiro, José

Filipe, Joaquim

Inhaltsverzeichnis

1;Preface;5
2;Organization;6
3;Table of Contents;9
4;Part I Internet Technology;12
4.1;Collaboration and Human Factor as Drivers for Reputation System Effectiveness;13
4.1.1;Introduction;13
4.1.2;Reputation and P2P Systems;14
4.1.3;Model Framework;15
4.1.4;Model Specifications and Parameters;16
4.1.4.1;The Reputation Model;17
4.1.4.2;The User Model;18
4.1.5;Results;19
4.1.5.1;Comeback Mode: Whitewashing;23
4.1.5.2;Scaling Issue;24
4.1.6;Discussion and Conclusions;24
4.1.7;References;26
4.2;Agent-Oriented Programming for Client-Side Concurrent Web 2.0 Applications;27
4.2.1;Introduction;27
4.2.2;The Agents and Artifacts Programming Model;29
4.2.2.1;Agent-Artifact Interaction: Use and Observation;31
4.2.3;An Agent-Oriented Model for Client-Side Web Programming;32
4.2.3.1;From {\tt simpA} to {\tt simpA-Web};33
4.2.4;A Sample Agent-Oriented Web 2.0 Application;34
4.2.4.1;Design;35
4.2.4.2;Implementation;36
4.2.5;Next Steps;37
4.2.6;Concluding Remarks;38
4.2.7;References;39
4.3;The SHIP: A SIP to HTTP Interaction Protocol;40
4.3.1;Introduction;40
4.3.1.1;Related Work;42
4.3.1.2;Structure of This Paper;42
4.3.2;System Architecture;42
4.3.2.1;Genaral Architecture;42
4.3.2.2;Integration of SHIP in IMS;44
4.3.3;Managing the HTTP Session;45
4.3.4;System Implementation;47
4.3.4.1;Session Setup;48
4.3.5;Application Example;48
4.3.6;Concluding Remarks;51
4.3.7;References;52
4.4;Efficient Authorization of Rich Presence Using Secure and Composed Web Services;54
4.4.1;Introduction;54
4.4.2;Related Work;56
4.4.3;Outline of Web Service Based Presence System;59
4.4.4;Presence Data Model;59
4.4.5;Extended RBAC Model;61
4.4.6;Implementation;63
4.4.7;Conclusions;65
4.4.8;References;66
5;Part II Web Interfaces and Applications;68
5.1;Information Supply of Related Papers from the Web for Scholarly e-Community;69
5.1.1;Introduction;69
5.1.2;Definition of Links into the Future;71
5.1.3;Related Work;71
5.1.4;Identifying Future Links from Web;72
5.1.4.1;Link Extraction;73
5.1.4.2;Removing Duplicates;73
5.1.4.3;Identifying Research Papers;73
5.1.4.4;Similarity Algorithm for Checking That Papers Are in the Same Area;74
5.1.4.5;Determining Future Links;75
5.1.4.6;Case Study;76
5.1.5;Citation Mining;77
5.1.6;Discussion;78
5.1.7;Conclusions;79
5.1.8;References;79
5.2;When Playing Meets Learning: Methodological Framework for Designing Educational Games;81
5.2.1;Introduction;81
5.2.1.1;When Playing Meets Learning: The Appeal of Game-Based Learning;82
5.2.1.2;Interdisciplinary Research-Project ELEKTRA;82
5.2.1.3;The ELEKTRA Methodology: Overview;82
5.2.2;Base of the Methodology: ELEKTRA’s 4Ms;83
5.2.2.1;M1 - Macroadaptivity;84
5.2.2.2;M2 - Microadaptivity;84
5.2.2.3;M3 - Metacognition;86
5.2.2.4;M4 - Motivation;86
5.2.3;Description of the Eight Phases;87
5.2.3.1;Phase 1: Identify Instructional Goals;88
5.2.3.2;Phase 2: Instructional Analysis;88
5.2.3.3;Phase 3: Analyse Learners and Context of Learning;88
5.2.3.4;Phase 4: Write Performance Objective and Overall Structure of the Game;89
5.2.3.5;Phase 5: Learning Game Design;89
5.2.3.6;Phase 6: Production and Development;90
5.2.3.7;Phase 7: Evaluation of Learning;90
5.2.3.8;Phase 8: Revise Instructions;91
5.2.4;Conclusions;91
5.2.5;References;92
5.3;SiteGuide: A Tool for Web Site Authoring Support;94
5.3.1;Introduction;94
5.3.2;Functionality of the SiteGuide Tool;95
5.3.3;Method;97
5.3.3.1;Quality Measure for Example Site Mappings;97
5.3.3.2;Finding a Good Mapping;98
5.3.3.3;FromMapping to Model;99
5.3.4;Evaluation;100
5.3.4.1;Evaluation of the Mappings;101
5.3.4.2;Evaluation of the Topic Descriptions;103
5.3.4.3;Evaluation ofWeb Site Design Support;104
5.3.5;Related Work;105
5.3.6;Conclusions;105
5.3.7;References;106
5.4;ArhiNet – A Knowledge-Based System for Creating, Processing and Retrieving Archival eContent;107
5.4.1;Introduction;107
5.4.2;Related Work;108
5.4.3;The Archival Domain – Corpus, Model and Domain Ontology;109
5.4.3.1;Archival Document Corpus;109
5.4.3.2;Archival Domain Model;110
5.4.3.3;The Core of the Domain Ontology;111
5.4.4;The ArhiNet System;112
5.4.4.1;System Architecture;113
5.4.4.2;Knowledge Acquisition;113
5.4.4.3;OWL Ontology to Hierarchical Data Mapping;115
5.4.4.4;Knowledge Processing and Retrieving;115
5.4.5;Case Study;116
5.4.6;Conclusions and Future Work;119
5.4.7;References;120
5.5;Optimizing Search and Ranking in Folksonomy Systems by Exploiting Context Information;121
5.5.1;Introduction;121
5.5.2;Related Work;122
5.5.3;GroupMe! Folksonomy System;122
5.5.4;Folksonomies;123
5.5.4.1;Folksonomy Characteristics in GroupMe!;124
5.5.5;Folksonomy-Based Ranking Algorithms;125
5.5.5.1;Universal Ranking Strategies;125
5.5.5.2;Ranking Resources;127
5.5.5.3;Synopsis;129
5.5.6;Evaluations;129
5.5.6.1;Metrics and Test Set;129
5.5.6.2;Base Set Detection;130
5.5.6.3;Experiment;131
5.5.6.4;Results;131
5.5.6.5;Optimizing GRank;132
5.5.7;Conclusions;134
5.5.8;References;134
5.6;Adaptation of the Domain Ontology for Different User Profiles: Application to Conformity Checking in Construction;136
5.6.1;Introduction;136
5.6.2;Three Motivations and Approaches to the Adaptation of Domain Ontologies for Different User Profiles;138
5.6.3;Our Approach for the Adaptation of the Domain Ontology to Different User Profiles;139
5.6.3.1;Knowledge Representation and Acquisition Method;139
5.6.3.2;Context Modeling of the Domain Ontology by Integrating the Results of Semantic Search;141
5.6.3.3;Towards Adaptation of the Domain Ontology to Different User Profiles;145
5.6.4;Implementation: Profiling the C3R Prototype;146
5.6.5;Conclusions;147
5.6.6;References;148
5.7;The RDF Protune Policy Editor: Enabling Users to Protect Data in the Semantic Web;150
5.7.1;Introduction;150
5.7.2;The Personal Reader Framework;151
5.7.2.1;The User Modeling Service;152
5.7.2.2;Policies for Securing Data;153
5.7.3;Protune Policy Templates for a User Modeling Service;153
5.7.3.1;Policy Templates for an RDF Based User Profile;154
5.7.4;The Back-End Policy Database;156
5.7.4.1;A Naive Implementation;156
5.7.4.2;Optimizations;157
5.7.5;User Interface for Defining Access Policies;158
5.7.5.1;Current Implementation;161
5.7.6;Evaluation of the Interface;161
5.7.6.1;Results;162
5.7.7;Related Work;163
5.7.8;Conclusions and Further Work;163
5.7.9;References;164
5.8;An Unsupervised Rule-Based Method to Populate Ontologies from Text;165
5.8.1;Introduction;165
5.8.2;Background;166
5.8.2.1;Information Extraction;166
5.8.2.2;Ontology Population;167
5.8.3;Dictionary Description;167
5.8.4;Ontology Population;169
5.8.4.1;Pre-processing;169
5.8.4.2;Information Extraction;170
5.8.4.3;Ontology Preparation;171
5.8.4.4;Ontology Instantiation;173
5.8.5;Implementation;173
5.8.6;Results;174
5.8.7;Discussion;175
5.8.7.1;Related Work;175
5.8.7.2;Future Work;175
5.8.8;References;176
5.9;Web Spam, Social Propaganda and the Evolution of Search Engine Rankings;178
5.9.1;Introduction;178
5.9.2;Web Spam;180
5.9.3;On Propaganda Theory;181
5.9.4;The Webgraph as a Trust Network;182
5.9.5;Search Engine Evolution;184
5.9.6;Conclusions;187
5.9.7;References;189
6;Part III Society, e-Business and e-Government;191
6.1;Making the Invisible Visible: Design Guidelines for Supporting Social Awareness in Distributed Collaboration;192
6.1.1;Introduction;192
6.1.2;Social Psychological Perspectives on Group Work;193
6.1.3;Related Work: Representing Social Activities;194
6.1.4;Social Awareness Design Guidelines;195
6.1.5;Design and Evaluation of an Enhanced Awareness Support;197
6.1.5.1;CommSy;197
6.1.5.2;Designing Awareness Functions;198
6.1.5.3;Evaluation;199
6.1.6;Conclusions and Future Work;201
6.1.7;References;203
6.2;Interaction Promotes Collaboration and Learning: Video Analysis of Algorithm Visualization Use during Collaborative Learning;205
6.2.1;Introduction;205
6.2.2;Previous Work;207
6.2.2.1;Engagement;207
6.2.2.2;TRAKLA2;207
6.2.2.3;Our Previous Studies on the Same Topic;209
6.2.3;Methodology;210
6.2.3.1;Participants;211
6.2.3.2;Procedure;211
6.2.3.3;Method;212
6.2.4;Results;213
6.2.5;Discussion;215
6.2.6;Conclusions;216
6.2.6.1;Future Directions;216
6.2.7;References;217
6.3;Modelling the B2C Marketplace: Evaluation of a Reputation Metric for e-Commerce;219
6.3.1;Introduction;219
6.3.2;Reputation System Framework;220
6.3.3;Reputation Metric;221
6.3.3.1;Compulsory Reputation;221
6.3.3.2;Optional Reputation;225
6.3.4;Simulating B2C e-Commerce Reputation System;225
6.3.4.1;Modelling the Buyers;225
6.3.4.2;Modelling the Providers;226
6.3.4.3;The Simulation Cycle;227
6.3.4.4;Modelling the Transaction and Rating Processes;227
6.3.4.5;Simulation Parameters;228
6.3.5;Evaluation Criteria;229
6.3.6;Simulation Results;229
6.3.7;Conclusions;231
6.3.8;References;232
7;Part IV Web Intelligence;234
7.1;Using Scientific Publications to Identify People with Similar Interests;235
7.1.1;Introduction;235
7.1.2;Related Work;236
7.1.3;Investigation;238
7.1.3.1;Text Parts: Title vs. Abstracts vs. Keywords vs. Complete Text;239
7.1.3.2;Text Indexes: Terms vs. Concepts;239
7.1.3.3;Similarity Function: Jaccard vs. Fuzzy;240
7.1.4;Experiments and Evaluations;241
7.1.5;Application Scenarios;243
7.1.5.1;Recommender Systems;243
7.1.5.2;Conference Reviewers;243
7.1.5.3;New Members for a Research Team;244
7.1.6;Concluding Remarks;244
7.1.7;References;246
7.2;Website-Level Data Extraction;248
7.2.1;Introduction;248
7.2.2;Related Work;249
7.2.3;Website Level Data Mining;250
7.2.3.1;Web Page Filtering;251
7.2.3.2;Web Page Clustering;253
7.2.3.3;Result Refinement;255
7.2.3.4;Object Identification;256
7.2.4;Evaluation;257
7.2.4.1;Task and Data Set;257
7.2.4.2;Evaluation Criteria;257
7.2.4.3;Experiment Results;258
7.2.5;Discussion;259
7.2.6;Conclusions;260
7.2.7;References;261
7.3;Anti-folksonomical Recommender System for Social Bookmarking Service;262
7.3.1;Introduction;262
7.3.2;Related Study;263
7.3.2.1;Social Bookmarking (SBM);263
7.3.2.2;Conventional Study on SBM;264
7.3.2.3;Conventional System Based on Co-occurrence of Items;265
7.3.3;Proposed System;266
7.3.3.1;Recommendation Based on SBM by Using “Item Cluster”;266
7.3.3.2;Model of Item Cluster;266
7.3.3.3;Procedure of the Proposed System;268
7.3.4;Experiments;268
7.3.4.1;Performance Evaluation;268
7.3.4.2;Comparison 1: Recommendation Based on Folksonomy;271
7.3.4.3;Comparison 2: Similarity by Jaccard Coefficient;272
7.3.5;Conclusions;274
7.3.6;References;274
7.4;Classifying Structured Web Sources Using Support Vector Machine and Aggressive Feature Selection;276
7.4.1;Introduction;276
7.4.2;Related Work;277
7.4.3;Classification Process;278
7.4.4;Feature Selection;279
7.4.4.1;Feature Selection Metrics;279
7.4.4.2;Feature Selection Procedure;281
7.4.5;Weighting Scheme and SVM Kernel;282
7.4.6;Experiments;283
7.4.6.1;Dataset and GPC Implementation;283
7.4.6.2;PerformanceMeasures and Feature Selection Methods;283
7.4.6.3;The Effect of Feature Selection;284
7.4.6.4;Comparision of FS Methods;285
7.4.6.5;Computational Efforts;286
7.4.7;Conclusions;287
7.4.8;References;287
7.5;Scalable Faceted Ranking in Tagging Systems;289
7.5.1;Introduction;289
7.5.2;Related Work;291
7.5.3;Two Tagging Systems: YouTube and Flickr;292
7.5.3.1;Analysis of the Recommendation Graphs;293
7.5.4;Algorithms for Faceted Ranking;295
7.5.4.1;Complexity Analysis of the Algorithms;297
7.5.5;Experimental Results and Discussion;298
7.5.6;Conclusions and Future Work;301
7.5.7;References;301
7.6;Answering Definition Questions: Dealing with Data Sparseness in Lexicalised Dependency Trees-Based Language Models;303
7.6.1;Introduction;303
7.6.2;Related Work;305
7.6.3;Our Approach;306
7.6.3.1;Grouping Sentences According to their Contexts Indicators;306
7.6.3.2;Building Contextual LanguageModels;308
7.6.3.3;Extracting Candidate Answers;310
7.6.4;Experiments and Results;312
7.6.4.1;Evaluation Metrics;312
7.6.4.2;Experimental Results;313
7.6.5;Conclusions;314
7.6.6;References;315
8;Author Index;317