Du / Ensan Canadian Semantic Web

Technologies and Applications
1. Auflage 2010
ISBN: 978-1-4419-7335-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 217 Seiten

The emergence of Web technologies for the distribution of an immense amount of data and knowledge has given rise to the need for supportive frameworks for kno- edge management. Semantic Web technologies aim at providing shared semantic spaces for Web contents, such that people, applications and communities can use a common platform to share information. Canadian Semantic Web: Technologies and Applications aims at contributing to the advancement of the Semantic Web by providing the most recent signi?cant - search on Semantic Web theory, techniques and applications in academia, industry and government in Canada and all over the world. It also enlightens possible - mantic Web research directions in future by reporting some works in-progress that presenton-goingresearchonprinciplesandapplicationsoftheSemanticWeb,while their implementation or deployment may have not been completed. This book consists of ten chapters. The chapters are extended versions of a - lected set of papers from the second Canadian Semantic Web Working Symposium (CSWWS 2009) and the twenty-?rst international Conference on Software En- neering and Knowledge Engineering (SEKE 2009). CSWWS 2009 was held in Kelowna, British Columbia in May 2009. Since many of the challenging aspects of the research problems tackled in the Semantic Web area fall in the realm of Ar- ?cial Intelligence or employ of AI techniques, CSWWS 2009 was organized in - nd sociation with the 22 Canadian Conference on Arti?cial Intelligence.

Autoren/Hrsg.

Du, Weichang

Ensan, Faezeh

Inhaltsverzeichnis

1;Preface;6
2;Program Committee;10
3;Contents;12
4;Chapter 1 Incremental Query Rewriting with Resolution;18
4.1;1.1 Introduction.;18
4.1.1;1.1.1 Settings and motivation.;18
4.1.2;1.1.2 Outline of the proposed method.;21
4.2;1.2 Informal method description.;22
4.3;1.3 Soundness and completeness of schematic answer computation.;27
4.4;1.4 Recording literals as search space pruning constraints.;31
4.5;1.5 SQL generation.;33
4.6;1.6 Implementation and experiments.;34
4.7;1.7 A note on indexing SemanticWeb documents with data abstractions.;37
4.8;1.8 Related work.;38
4.9;1.9 Summary and future work.;40
4.10;References;42
5;Chapter 2 Knowledge Representation and Reasoning in Norm-Parameterized Fuzzy Description Logics;44
5.1;2.1 Introduction;44
5.2;2.2 Preliminaries;48
5.3;2.3 Fuzzy Set Theory and Fuzzy Logic;49
5.4;2.4 Fuzzy Description Logic;51
5.4.1;2.4.1 Syntax of fALCN;52
5.4.2;2.4.2 Semantics of fALCN;52
5.4.3;2.4.3 Knowledge Bases in fALCN;55
5.5;2.5 Reasoning Tasks;56
5.6;2.6 GCI, NNF, and ABox Augmentation;58
5.7;2.7 Reasoning Procedure;60
5.8;2.8 Soundness, Completeness, and Termination of the Reasoning Procedure for fALCN;62
5.9;2.9 Conclusion and FutureWork;68
5.10;References;69
6;Chapter 3 A Generic Evaluation Model for Semantic Web Services;71
6.1;3.1 Introduction;71
6.2;3.2 Performance Engineering for Component- and Service-oriented Systems;73
6.3;3.3 Requirements for a Generic Evaluation Model;74
6.3.1;3.3.1 Openness;74
6.3.2;3.3.2 Tool Independent;75
6.3.3;3.3.3 Conciseness;75
6.3.4;3.3.4 Preciseness;75
6.3.5;3.3.5 Completeness;75
6.3.6;3.3.6 Based on Classical Problems;76
6.3.7;3.3.7 Different Complexity Levels;76
6.3.8;3.3.8 Common Benchmarking;76
6.3.9;3.3.9 Flexibility to Perform Remote Evaluation;77
6.4;3.4 A Generic Evaluation Model for SemanticWeb Services;77
6.4.1;3.4.1 Semantic Web Services Execution Lifecycle;78
6.4.1.1;3.4.1.1 Service Discovery - S1;78
6.4.1.2;3.4.1.2 Service Selection - S2;78
6.4.1.3;3.4.1.3 Service Composition - S3;78
6.4.1.4;3.4.1.4 Service Mediation - S4;78
6.4.1.5;3.4.1.5 Service Choreography and Orchestration - S5;79
6.4.1.6;3.4.1.6 Service Invocation - S6;79
6.4.1.7;3.4.1.7 External Communication - S7;79
6.4.1.8;3.4.1.8 Internal Execution Management Time - EM;79
6.4.1.9;3.4.1.9 Overall Execution Time - T;80
6.4.2;3.4.2 Critical Evaluation Factors;80
6.4.2.1;3.4.2.1 Response Time - C1;80
6.4.2.2;3.4.2.2 Resource Consumption - C2;80
6.4.2.3;3.4.2.3 Resource Availability - C3;81
6.4.2.4;3.4.2.4 Service Availability - C4;81
6.4.2.5;3.4.2.5 Meaningfulness of Results - C5;81
6.4.2.6;3.4.2.6 Correctness of Results - C6;81
6.4.2.7;3.4.2.7 Completeness of Results - C7;82
6.4.2.8;3.4.2.8 Consistency of Results - C8;82
6.4.2.9;3.4.2.9 Degree of Decoupling - C9;82
6.5;3.5 Using the Evaluation Model for Semantic Web Services based on TSC;85
6.5.1;3.5.1 Comparing Resource Availability;85
6.5.2;3.5.2 Analyzing Performance on Concurrent Execution of Goals;86
6.5.3;3.5.3 Comparing Communication Overhead;86
6.5.4;3.5.4 Communication Overhead vs. Time Saved in Multiple Goal Execution;86
6.5.5;3.5.5 Comparing Time Taken in Distributed Service Execution;87
6.5.6;3.5.6 Comparing Time Saved by Applications while Executing aGoal;87
6.5.7;3.5.7 Comparing Time Saved in Resource Retrieval by WSMX;88
6.6;3.6 RelatedWork;88
6.6.1;3.6.1 Semantic Web Challenge;88
6.6.2;3.6.2 Semantic Web Services Challenge;89
6.6.3;3.6.3 Semantic Service Selection (S3);89
6.6.4;3.6.4 IEEE Web Services Challenge;89
6.6.5;3.6.5 SEALS Evaluation Campaigns;90
6.6.6;3.6.6 STI International Test Beds and Challenges Service;90
6.6.7;3.6.7 International Rules Challenge at RuleML;91
6.7;3.7 Conclusions and FutureWork;91
6.8;Acknowledgments.;92
6.9;References;92
7;Chapter 4 A Modular Approach to Scalable Ontology Development;94
7.1;4.1 Introduction;94
7.2;4.2 Interface-Based Modular Ontologies;97
7.2.1;4.2.1 The Formalism;97
7.2.2;4.2.2 IBF: Scalability and Reasoning Performance;98
7.3;4.3 OWL Extension and Tool Support for the Interface-Based Modular Ontology Formalism;98
7.4;4.4 Evaluating IBF Modular Ontologies;104
7.4.1;4.4.1 cohesion;104
7.4.2;4.4.2 coupling;106
7.4.3;4.4.3 Knowledge Encapsulation;109
7.5;4.5 Case Studies;109
7.5.1;4.5.1 IBF Modular Ontologies;110
7.5.2;4.5.2 IBF Ontologies Analysis;112
7.6;4.6 Related Work;114
7.7;4.7 Conclusion;115
7.7.1;References;116
7.8;4.8 Appendix;118
8;Chapter 5 Corporate SemanticWeb: Towards the Deployment of Semantic Technologies in Enterprises;119
8.1;5.1 Introduction;119
8.2;5.2 Application Domains for a Corporate SemanticWeb;120
8.3;5.3 Gaps;122
8.4;5.4 Corporate SemanticWeb;123
8.5;5.5 Corporate Ontology Engineering;125
8.5.1;5.5.1 Modularization and Integration Dimensions of COLM;126
8.5.1.1;5.5.1.1 Technical Concept;127
8.5.2;5.5.2 Versioning Dimensions of COLM;128
8.5.2.1;5.5.2.1 Design of the SVoNt Version Control System for OWL Ontologies;129
8.6;5.6 Corporate Semantic Collaboration;130
8.6.1;5.6.1 Editor Functionalities;131
8.6.2;5.6.2 User Groups;132
8.6.3;5.6.3 Design of the Light-weight Ontology Editor;133
8.7;5.7 Corporate Semantic Search;135
8.7.1;5.7.1 Search in Non-Semantic Data;136
8.7.1.1;5.7.1.1 Collecting Knowledge with Extreme Tagging Approach;136
8.7.1.2;5.7.1.2 Preprocessing Texts by Parsing and Chunking;138
8.7.2;5.7.2 Semantic Search Personalization;139
8.7.2.1;5.7.2.1 Semantic Matchmaking Framework;140
8.8;5.8 Conclusion and Outlook;144
8.9;References;144
9;Chapter 6 Semantic Service Matchmaking in the ATM Domain Considering Infrastructure Capability Constraints;146
9.1;6.1 Introduction;146
9.2;6.2 RelatedWork;149
9.2.1;6.2.1 Technical Integration;149
9.2.2;6.2.2 Semantic Integration with Semantic Web Services;151
9.2.3;6.2.3 Service Matchmaking Approaches;155
9.3;6.3 Research Issues;156
9.4;6.4 ATM Scenario Description;157
9.5;6.5 Semantic Service Matchmaking Approach;160
9.5.1;6.5.1 Identification of Possible Collaboration Candidate Sets;160
9.5.2;6.5.2 Validity-Check and Optimization of Collaborations;162
9.6;6.6 Case Study;163
9.6.1;6.6.1 Discussion;164
9.7;6.7 Conclusion;166
9.8;References;168
10;Chapter 7 Developing Knowledge Representation in Emergency Medical Assistance by Using Semantic Web Techniques;171
10.1;7.1 Introduction;171
10.2;7.2 Ontology and Mobile Devices Background;172
10.3;7.3 Proposed Approach;174
10.3.1;7.3.1 Ontology Development;175
10.3.1.1;7.3.1.1 Ontology Modeling;176
10.3.2;7.3.2 Determining the Ontology Domain;176
10.3.3;7.3.3 Enumerating Important Terms, Classes and the Class Hierarchy;177
10.3.4;7.3.4 Defining Properties and Restrictions of Classes;178
10.3.5;7.3.5 Creating Instances and New Terms Extraction;178
10.3.5.1;7.3.5.1 New Terms Instantiation;180
10.3.5.2;7.3.6 Semantic Cache;182
10.4;7.4 Experimental Environment and Results;183
10.5;7.5 Conclusions and FutureWork;183
10.6;References;185
11;Chapter 8 Semantically Enriching the Search System of a Music Digital Library;187
11.1;8.1 Introduction;187
11.2;8.2 Research Context;189
11.2.1;8.2.1 Previous Work;189
11.2.2;8.2.2 Cantiga Project;189
11.3;8.3 MagisterMusicae Search System;190
11.4;8.4 Improving Searchability;191
11.4.1;8.4.1 Applying Semantic Web Technologies;191
11.4.1.1;8.4.1.1 The Domain Ontology;192
11.4.1.2;8.4.1.2 The Instrument Taxonomy;193
11.4.1.3;8.4.1.3 The Resources Ontology;195
11.4.1.4;8.4.1.4 The Concept Taxonomy;195
11.4.2;8.4.2 Linking the Ontology with External Data Sources;197
11.4.2.1;8.4.2.1 Geographical Enrichment;198
11.4.2.2;8.4.2.2 Lexical Enrichment;198
11.4.3;8.4.3 Alternative Search Paradigms;199
11.5;8.5 Cantiga Semantic Search System;200
11.5.1;8.5.1 Details on the implementation;201
11.6;8.6 Evaluation;202
11.7;8.7 RelatedWork;204
11.8;8.8 Conclusions and FutureWork;205
11.9;Acknowledgments;205
11.10;References;205
12;Chapter 9 Application of an Intelligent System Frameworkand the SemanticWeb for the CO2 Capture Process;207
12.1;9.1 Introduction;207
12.2;9.2 Backgroundt;208
12.2.1;9.2.1 Application Problem Domain;208
12.2.2;9.2.2 Ontology and Semantic Web;208
12.3;9.3 Knowledge Modeling and Ontology Construction;209
12.3.1;9.3.1 Ontology Design;209
12.3.2;9.3.2 Ontology Management;211
12.4;9.4 Intelligent System Framework;212
12.5;9.5 Application of the Semantic Knowledge AWeb-based Expert System;214
12.6;9.6 Conclusion and FutureWork;215
12.7;Acknowledgments;217
12.8;References;217
13;Chapter 10 Information Pre-Processing using Domain Meta-Ontology and Rule Learning System;218
13.1;10.1 Introduction;219
13.2;10.2 The domain meta-ontology;221
13.3;10.3 The system for semi-automatic population of domain meta-ontology;224
13.4;10.4 Details of the Rule Learning System flow;225
13.5;References;227