Giunchiglia / Tanca Semantic Web Information Management

A Model-Based Perspective
1. Auflage 2010
ISBN: 978-3-642-04329-1
Verlag: Springer
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 549 Seiten

Databases have been designed to store large volumes of data and to provide efficient query interfaces. Semantic Web formats are geared towards capturing domain knowledge, interlinking annotations, and offering a high-level, machine-processable view of information. However, the gigantic amount of such useful information makes efficient management of it increasingly difficult, undermining the possibility of transforming it into useful knowledge. The research presented by De Virgilio, Giunchiglia and Tanca tries to bridge the two worlds in order to leverage the efficiency and scalability of database-oriented technologies to support an ontological high-level view of data and metadata. The contributions present and analyze techniques for semantic information management, by taking advantage of the synergies between the logical basis of the Semantic Web and the logical foundations of data management. The book's leitmotif is to propose models and methods especially tailored to represent and manage data that is appropriately structured for easier machine processing on the Web. After two introductory chapters on data management and the Semantic Web in general, the remaining contributions are grouped into five parts on Semantic Web Data Storage, Reasoning in the Semantic Web, Semantic Web Data Querying, Semantic Web Applications, and Engineering Semantic Web Systems. The handbook-like presentation makes this volume an important reference on current work and a source of inspiration for future development, targeting academic and industrial researchers as well as graduate students in Semantic Web technologies or database design.

Roberto De Virgilio is with Università di Roma Tre as PostDoc fellow under the supervision of Riccardo Torlone. The last years his research focuses on Semantic Web information management at different levels of abstraction. Fausto Giunchiglia is professor of computer science at the University of Trento, Department of Information and Communication Technology, ECCAI Fellow. His research has covered many different areas: knowledge representation, context and reasoning with context, knowledge management and peer-to-peer knowledge, semantic web, formal methods, theorem proving, and model checking. Letizia Tanca is a full professor with Politecnico di Milano. Her research interests range over all database theory, especially on deductive, active and object oriented databases, graph-based languages for databases, and the semantics of advanced database and information systems, representation and querying of semistructured information. Her most recent research interests concern context-aware database design, data integration, schema evolution, mobile databases and very small databases for mobile devices.

Autoren/Hrsg.

Giunchiglia, Fausto

Tanca, Letizia

Inhaltsverzeichnis

1;Preface;5
2;Contents;6
3;Contributors;16
4;Introduction;20
4.1;PART 1. Semantic Web Data Storage;21
4.2;PART 2. Reasoning in the Semantic Web;22
4.3;PART 3. Semantic Web Data Querying;23
4.4;PART 4. Semantic Web Applications;25
4.5;PART 5. Engineering Semantic Web Systems;26
5;Data and Metadata Management;27
5.1;Introduction;27
5.2;Databases, Schemas and Dictionaries;28
5.3;Data Models;30
5.4;Management of Multiple Models;32
5.5;Model-generic Schema Translation;36
5.6;Related Work;39
5.7;Conclusion;40
5.8;References;40
6;The Semantic Web Languages;43
6.1;Introduction;43
6.2;The Hierarchy of Languages;45
6.2.1;XML: Raw Data-No Semantics;45
6.2.2;RDF(S): Representing Objects and Relations Among Them;46
6.2.3;OWL: Ontologies-Representing Classes and Relations Among Them;46
6.2.4;C-OWL: Contextual Ontologies-Representing Context Mappings;47
6.3;RDF(S);47
6.4;OWL;49
6.4.1;OWL Lite;49
6.4.2;OWL DL;50
6.4.3;OWL Full;50
6.5;C-OWL;51
6.6;Semantic Web and Databases;52
6.7;Conclusion;54
6.8;Appendix A: RDF(S) Constructs;54
6.9;Appendix B: OWL Constructs;54
6.10;References;55
7;Semantic Web Data Storage;57
7.1;Relational Technologies, Metadata and RDF;58
7.1.1;Introduction;58
7.1.2;The Relational Model;61
7.1.3;Modeling Metadata in Relational Systems;62
7.1.3.1;Separate Structures;62
7.1.3.2;Intermixed Context;62
7.1.3.3;Intensional Associations;64
7.1.4;RDF;65
7.1.5;Using Relational Systems for RDF Storage;67
7.1.5.1;Storing RDF as XML;68
7.1.5.2;Vertical Table;70
7.1.5.3;Graph-based Storage;71
7.1.5.4;Graph Schema-Vertical Data;73
7.1.5.5;Property Table;75
7.1.5.6;Vertical Partitioning;77
7.1.5.7;Smart Indexing;79
7.1.6;Conclusion;81
7.1.7;References;81
7.2;A Metamodel Approach to Semantic Web Data Management;84
7.2.1;Introduction;84
7.2.2;Motivating Example;86
7.2.3;Management of RDF Data;87
7.2.3.1;A Conceptual Representation;89
7.2.3.2;Logical Level;92
7.2.3.3;Physical Level;94
7.2.3.4;Query Processing;96
7.2.4;Related Work;96
7.2.5;Experimental Results;99
7.2.5.1;RDF Benchmark;99
7.2.5.1.1;Query 1 (Q1);100
7.2.5.1.2;Query 2 (Q2);100
7.2.5.1.3;Query 3 (Q3);101
7.2.5.1.4;Query 4 (Q4);101
7.2.5.1.5;Query 5 (Q5);101
7.2.5.1.6;Query 6 (Q6);102
7.2.5.2;Platform Environment;102
7.2.5.3;Evaluating Results;103
7.2.5.3.1;Performance Results;103
7.2.5.3.2;Q1;104
7.2.5.3.3;Q2;104
7.2.5.3.4;Q3;104
7.2.5.3.5;Q4;105
7.2.5.3.6;Q5;105
7.2.5.3.7;Q6;105
7.2.5.3.8;Scalability Results;105
7.2.6;Conclusion;106
7.2.7;References;107
7.3;Managing Terabytes of Web Semantics Data;109
7.3.1;Introduction;109
7.3.1.1;Definition and Model for the Web of Data;110
7.3.2;Providing Services on the Web of Data: A Model for a Large Scale Semantic Data Processing Infrastructure;111
7.3.3;Semantic Sitemap: A Dataset Publishing Model;113
7.3.3.1;The Sitemap Protocol and robots.txt;114
7.3.3.2;The Semantic Sitemaps Extension;114
7.3.3.2.1;Datasets;115
7.3.3.2.2;Adding Dataset Descriptions to the Sitemap Protocol;115
7.3.4;Pre-processing: Context-dependent Reasoning;116
7.3.4.1;Contexts on the Semantic Web;117
7.3.4.1.1;Aggregate Context;118
7.3.4.1.2;Lifting Rules;118
7.3.4.2;Import Closure of RDF Models;118
7.3.4.3;Deductive Closure of RDF Models;119
7.3.4.4;Technical Overview;120
7.3.4.5;Performance Overview;121
7.3.4.6;Discussion;121
7.3.5;Indexing: The SIREn Model;122
7.3.5.1;SIREn Data Model;122
7.3.5.2;SIREn Query Model;124
7.3.5.2.1;SIREn Operators;124
7.3.5.2.1.1;Content Operators;124
7.3.5.2.2;SPARQL Interpretation;125
7.3.5.3;Experimental Benchmark;126
7.3.6;Ranking: The DING Model;128
7.3.6.1;A Two-layer Model for Web Data;129
7.3.6.2;DING Algorithm;130
7.3.6.3;Combining DING and Entity Rank;131
7.3.7;Leveraging Sindice in SIGMA;131
7.3.7.1;Sig.ma: Processing Dataflow and Interaction with Sindice;132
7.3.7.1.1;Creation of a Sig.ma Query Plan;133
7.3.7.1.2;Data Sources Selection;134
7.3.7.1.3;Extraction and Alignment of Related Subgraphs;134
7.3.7.1.4;Consolidation;135
7.3.7.1.4.1;Value Labelling, Consolidation and Source List Refinement;136
7.3.8;Conclusion;136
7.3.9;References;138
8;Reasoning in the Semantic Web;140
8.1;Reasoning in Semantic Web-based Systems;141
8.1.1;Introduction;141
8.1.2;Background;142
8.1.2.1;Logic Programming;142
8.1.2.2;Description Logics;144
8.1.3;Standards on the Semantic Web;145
8.1.3.1;OWL 2;145
8.1.3.2;WSML;146
8.1.4;Reasoning Techniques;147
8.1.4.1;Description Logic Paradigm;147
8.1.4.1.1;Tableaux Methods;147
8.1.4.1.1.1;Pellet;148
8.1.4.1.2;Translation to Rule Based Systems;149
8.1.4.1.2.1;KAON2;149
8.1.4.2;Rule Paradigm;149
8.1.4.2.1;Bottom-up Techniques;150
8.1.4.2.2;Magic-sets Evaluation;151
8.1.4.2.2.1;IRIS;151
8.1.4.2.3;Top-down (SLD) Resolution with Memoing: SLG;152
8.1.4.2.3.1;XSB;153
8.1.5;Reasoning on the Web;153
8.1.5.1;Expressivity;154
8.1.5.2;Approximate Reasoning;156
8.1.5.2.1;Approximation of the Reasoning Method;156
8.1.5.2.2;Approximation of the Request;157
8.1.5.2.3;Approximation of the Knowledge Base;157
8.1.6;Conclusion;158
8.1.7;References;158
8.2;Modular Knowledge Representation and Reasoning in the Semantic Web;161
8.2.1;Introduction;161
8.2.2;Ontologies and Description Logics;163
8.2.2.1;Description Logic SHOIQb;164
8.2.2.2;Sublanguages of SHOIQb;166
8.2.3;Reference Distributed Ontology Framework;167
8.2.4;Distributed Description Logics;168
8.2.4.1;Formalization;168
8.2.4.2;Reasoning in DDL;170
8.2.4.3;Modeling with DDL;171
8.2.5;E-connections;174
8.2.5.1;Formalization;174
8.2.5.2;Reasoning in E-connections;178
8.2.5.3;Modeling with E-connections;179
8.2.6;Package-based Description Logics;182
8.2.6.1;Formalization;182
8.2.6.2;Reasoning in P-DL;184
8.2.6.3;Modeling with P-DL;184
8.2.7;Integrated Distributed Description Logics;187
8.2.7.1;Formalization;187
8.2.7.2;Reasoning in IDDL;189
8.2.7.3;Modeling with IDDL;189
8.2.8;Conclusion;191
8.2.9;References;193
8.3;Semantic Matching with S-Match;196
8.3.1;Introduction;196
8.3.2;State of the Art;197
8.3.2.1;Rondo;198
8.3.2.2;Cupid;198
8.3.2.3;COMA;198
8.3.3;Semantic Matching;199
8.3.3.1;The Tree Matching Algorithm;200
8.3.3.1.1;Step 1.;201
8.3.3.1.2;Step 2.;201
8.3.3.1.3;Step 3.;202
8.3.3.1.4;Step 4.;203
8.3.3.2;Node Matching Algorithm;205
8.3.4;Efficient Semantic Matching;206
8.3.4.1;Conjunctive Concepts at Nodes;207
8.3.4.1.1;Tests for Less and More General Relations;207
8.3.4.2;Disjunctive Concepts at Nodes;208
8.3.5;The S-Match Architecture;209
8.3.6;Evaluation;209
8.3.6.1;Evaluation Set Up;209
8.3.6.2;Evaluation Results;211
8.3.7;Conclusion;213
8.3.8;References;213
8.4;Preserving Semantics in Automatically Created Ontology Alignments;216
8.4.1;Introduction;216
8.4.2;Related Research Fields;217
8.4.3;Preliminaries;218
8.4.3.1;Ontologies;218
8.4.3.2;Correspondences and Mappings;219
8.4.3.3;Distributed Ontologies;220
8.4.3.4;Faults and Diagnoses;221
8.4.4;Mapping Revision;222
8.4.4.1;Model-based Mapping Revision;223
8.4.4.1.1;Consistency-Preserving Revision;223
8.4.4.1.2;Coherence-Preserving Revision;224
8.4.4.2;Heuristic Mapping Revision;226
8.4.4.2.1;Static Heuristic Revision;226
8.4.4.2.2;Heuristics for Coherence Preservation;227
8.4.4.2.2.1;Bowtie Rule (B-Rule);227
8.4.4.2.2.2;Partition Rule (P-Rule);228
8.4.4.2.2.3;Cycle Rule (CYC-Rule);229
8.4.4.2.2.4;Blind Multiple Correspondences;229
8.4.4.3;Uncertainty-Aware Mapping Revision;231
8.4.4.3.1;Probabilistic Mapping Revision;232
8.4.4.3.2;Fuzzy Mapping Revision;232
8.4.5;Conclusion;233
8.4.6;References;234
8.5;tOWL: Integrating Time in OWL;237
8.5.1;Introduction;237
8.5.2;Preliminaries;239
8.5.2.1;Concrete Domains;239
8.5.2.2;4D Fluents;240
8.5.3;tOWL;242
8.5.3.1;tOWL Overview;242
8.5.3.2;OWL Schema of tOWL;242
8.5.4;A tOWL Ontology for the Leveraged Buyouts;246
8.5.4.1;Leveraged Buyouts;247
8.5.4.2;TBox;247
8.5.4.3;ABox;251
8.5.4.4;Use Cases;253
8.5.5;Related Work;254
8.5.5.1;Temporal RDF;254
8.5.5.2;OWL-Time;255
8.5.5.3;4D Fluents;256
8.5.6;Conclusion;257
8.5.7;References;258
9;Semantic Web Data Querying;259
9.1;Datalog Extensions for Tractable Query Answering over Ontologies;260
9.1.1;Introduction;260
9.1.2;Preliminaries;263
9.1.2.1;Databases and Queries;264
9.1.2.2;Dependencies;264
9.1.2.3;The Chase;265
9.1.2.4;Treewidth;267
9.1.3;Guarded Datalog±;268
9.1.3.1;Combined Complexity;268
9.1.3.2;Data Complexity;270
9.1.4;Linear Datalog±;273
9.1.4.1;Combined Complexity;273
9.1.4.2;Data Complexity;274
9.1.5;Weakly Guarded Datalog±;275
9.1.5.1;Combined Complexity;276
9.1.5.2;Data Complexity;278
9.1.6;Extensions;279
9.1.6.1;Negative Constraints;279
9.1.6.2;Non-conflicting Keys;280
9.1.7;Ontology Querying;282
9.1.7.1;DL-LiteA;282
9.1.7.2;F-Logic Lite;286
9.1.8;Conclusion;288
9.1.9;References;289
9.2;On the Semantics of SPARQL;291
9.2.1;Introduction;291
9.2.2;The W3C Syntax of SPARQL;293
9.2.2.1;Basic Definitions;294
9.2.2.2;Basic Structures;294
9.2.2.3;More Complex Queries;296
9.2.2.4;Final Remarks;298
9.2.3;An Algebraic Syntax for SPARQL;299
9.2.3.1;Translating SPARQL into the Algebraic Formalism;301
9.2.4;Semantics of SPARQL;303
9.2.4.1;Blank Nodes in Graph Patterns;309
9.2.4.2;Bag Semantics of SPARQL;310
9.2.5;On the Complexity of the Evaluation Problem;311
9.2.6;Related Work;315
9.2.7;Conclusion;316
9.2.8;References;317
9.3;Labeling RDF Graphs for Linear Time and Space Querying;318
9.3.1;Introduction;319
9.3.1.1;Contributions;320
9.3.2;Motivating Example;321
9.3.3;Preliminaries-RDF as Graphs;323
9.3.3.1;Queries on RDF Graphs;324
9.3.3.2;Triple Patterns and Adjacency;327
9.3.4;Labeling Schemes for RDF Graphs;328
9.3.4.1;Foundation: Tree Labeling;328
9.3.4.2;Reachability in Graphs;331
9.3.5;cig-labeling Scheme;332
9.3.5.1;Label Size;334
9.3.5.2;Adjacency & Reachability Test;335
9.3.5.3;Optimal cig-labeling;336
9.3.6;cig-labeling on Trees and cigs;337
9.3.6.1;cigs: Sharing-Limited Graphs;337
9.3.6.2;Labeling cigs and Trees;339
9.3.6.3;Properties of cig-labelings on cigs and Trees;339
9.3.6.4;Limitations and Extensions;340
9.3.7;Processing SPARQL with cig-labelings;341
9.3.7.1;Processing 1-SPARQL;341
9.3.7.2;Processing A-SPARQL;342
9.3.7.3;Towards Full SPARQL;344
9.3.7.4;Towards Path Expressions;345
9.3.8;Conclusion;345
9.3.9;References;346
9.4;SPARQLog: SPARQL with Rules and Quantification;349
9.4.1;Introduction;350
9.4.1.1;Contributions;352
9.4.2;Preliminaries;352
9.4.3;SPARQL Rule Languages;355
9.4.3.1;SPARQL and Rule Extensions of SPARQL;355
9.4.3.2;Other Rule-based RDF Query Languages;356
9.4.3.3;Quantifier Alternation in Data Exchange;357
9.4.4;SPARQLog: SPARQL with Rules and Quantification;358
9.4.4.1;SPARQLog Syntax;360
9.4.4.2;Denotational Semantics for SPARQLog;361
9.4.4.3;Relational Operational Semantics for SPARLog;363
9.4.4.3.1;Soundness and Completeness;365
9.4.4.3.2;Proof of Lemma 15.2 and Theorem 15.1;366
9.4.5;Properties of SPARQLog;368
9.4.5.1;Designing Tractable Fragments of SPARQLog;368
9.4.5.1.1;SwARQLog;370
9.4.5.2;Expressiveness of Quantifier Alternation in SPARQLog;372
9.4.5.3;Experimental Comparison with SPARQL Engines;375
9.4.6;Conclusion;377
9.4.7;References;377
9.5;SP2Bench: A SPARQL Performance Benchmark;379
9.5.1;Introduction;379
9.5.1.1;Structure;381
9.5.2;Benchmark Design Decisions;382
9.5.2.1;Requirements for Domain-specific Benchmarks;382
9.5.3;The SP2Bench Data Generator;383
9.5.3.1;Characteristics of DBLP Data;383
9.5.3.1.1;Structure of Document Classes;383
9.5.3.1.2;Development of Document Classes over Time;385
9.5.3.1.3;Other Characteristics;386
9.5.3.2;Data Generator Implementation and RDF Scheme;387
9.5.3.2.1;The SP2Bench RDF Scheme for DBLP;388
9.5.4;The SP2Bench Benchmark Queries;390
9.5.4.1;RDF Characteristics;390
9.5.4.2;SPARQL Characteristics;392
9.5.4.3;Discussion of Benchmark Queries;392
9.5.4.3.1;Benchmark Query Q1:;393
9.5.4.3.2;Benchmark Query Q2:;393
9.5.4.3.3;Benchmark Queries Q3abc:;394
9.5.4.3.4;Benchmark Query Q4:;394
9.5.4.3.5;Benchmark Queries Q5ab:;395
9.5.4.3.6;Benchmark Query Q6:;395
9.5.4.3.7;Benchmark Query Q7:;396
9.5.4.3.8;Benchmark Query Q8:;396
9.5.4.3.9;Benchmark Query Q9:;397
9.5.4.3.10;Benchmark Query Q10:;397
9.5.4.3.11;Benchmark Query Q11:;397
9.5.4.3.12;Benchmark Query Q12:;398
9.5.5;Benchmark Metrics;398
9.5.6;Conclusion;399
9.5.7;References;400
10;Semantic Web Applications;402
10.1;Using OWL in Data Integration;403
10.1.1;Introduction;404
10.1.2;The Data Integration Framework;406
10.1.3;Computational Characterization of Query Answering;412
10.1.4;Data Integration Using OWL 2 QL;415
10.1.4.1;Schema-Rewriting;416
10.1.4.2;LAV-Rewriting;417
10.1.4.3;GAV-Rewriting;417
10.1.4.4;Source-Evaluation;418
10.1.5;Related Work;420
10.1.6;Conclusion;425
10.1.7;References;425
10.2;Service Knowledge Spaces for Semantic Collaboration in Web-based Systems;431
10.2.1;Introduction;431
10.2.2;Semantic Collaboration in Networked Web-based Systems;432
10.2.3;Service Knowledge Space;435
10.2.3.1;Preliminary Notions;435
10.2.3.2;Model-based Service Description;436
10.2.3.3;Ontological Infrastructure;437
10.2.3.3.1;Local Service Knowledge;437
10.2.3.3.2;Network Service Knowledge;440
10.2.4;Distributed Semantic Service Management;443
10.2.4.1;Distributed Service Registry Structure;444
10.2.4.2;Distributed Service Registry Maintenance;445
10.2.5;Semantic Collaboration with Distributed Service Registry;446
10.2.5.1;Selection of semantic neighbors;446
10.2.5.2;Request forwarding and collection of search results;447
10.2.5.3;Extending the search results through related services;447
10.2.5.4;Experimental Evaluation;448
10.2.6;Related Work;449
10.2.6.1;Ontology-based Service Description;449
10.2.6.2;Service Discovery and Matchmaking;450
10.2.6.3;P2P Service-based Semantic Collaboration;451
10.2.7;Conclusion;452
10.2.8;References;453
10.3;Informative Top-k Retrieval for Advanced Skill Management;455
10.3.1;Introduction;456
10.3.2;Preliminaries;457
10.3.2.1;Reference Formalism;457
10.3.2.2;Query Language;458
10.3.2.3;Top-k Retrieval;459
10.3.3;Human Resources Retrieval;459
10.3.3.1;Ontology Component;461
10.3.3.2;Database Component;463
10.3.3.3;Query Process (by Example);466
10.3.3.4;Match Explanation;468
10.3.4;Experiments;472
10.3.5;Related Work;474
10.3.5.1;Research Approaches;475
10.3.5.2;Commercial Tools;476
10.3.6;Appendix A: Ontology Axioms Excerpts;476
10.3.7;Appendix B: Example Candidate Profiles Set;479
10.3.8;References;481
11;Engineering Semantic Web Systems;483
11.1;MIDST: Interoperability for Semantic Annotations;484
11.1.1;Introduction;484
11.1.2;State of the Art;486
11.1.2.1;From Database to Ontology;487
11.1.2.2;From Ontology to Database;488
11.1.2.3;Translation Between Ontologies and Databases;488
11.1.3;Model Independent Schema and Data Translation;490
11.1.3.1;Basic Translations;491
11.1.3.2;More Complex Translations;492
11.1.4;OWL and Relational Database Interoperability;493
11.1.4.1;Relational Data Model;493
11.1.4.2;OWL Data Model;495
11.1.5;An Extended Supermodel;497
11.1.5.1;Management of Intersections;498
11.1.5.2;Management of Restrictions;499
11.1.5.3;Classes Equivalence;499
11.1.5.4;Properties Equivalence;499
11.1.5.5;Object and Datatype Properties Generalization;500
11.1.5.6;Functional Datatype Properties;500
11.1.5.7;Symmetric, Transitive and Inverse Object Properties;500
11.1.6;Translation Rules;500
11.1.7;Conclusion;503
11.1.8;References;504
11.2;Virtuoso: RDF Support in a Native RDBMS;506
11.2.1;Introduction;506
11.2.2;State of the Art;507
11.2.2.1;Vertical Layouts;507
11.2.2.2;Triples Indexed with Sorted Lists;507
11.2.2.3;Clustered Quad Stores;508
11.2.2.4;SPARQL Processor Without Local Data Storage;509
11.2.3;Triple Storage;509
11.2.3.1;Compression;510
11.2.3.2;Alternative Index Layouts;511
11.2.4;SPARQL and SQL;511
11.2.4.1;SQL Cost Model and RDF Queries;512
11.2.4.2;Basic RDF Inferencing;512
11.2.4.3;Data Manipulation;513
11.2.4.4;Full Text;513
11.2.4.5;Aggregation;513
11.2.4.6;RDF Sponge;513
11.2.5;Clustering and Scalability;514
11.2.5.1;Query Execution Model;515
11.2.5.2;Performance;516
11.2.6;Mapping Relational Data into RDF for SPARQL Access;517
11.2.7;Applications and Benchmarks;520
11.2.7.1;Web 2.0 Applications;520
11.2.7.2;OpenLink Data Spaces (ODS);520
11.2.7.3;Berlin SPARQL Benchmark;520
11.2.8;Future Directions;521
11.2.8.1;Clustering;521
11.2.8.2;Updating Relational Data by SPARUL Statements;521
11.2.9;Conclusion;522
11.2.10;References;523
11.3;Hera: Engineering Web Applications Using Semantic Web-based Models;525
11.3.1;Introduction;525
11.3.2;Method;527
11.3.2.1;Data Modeling;528
11.3.2.2;Application Modeling;529
11.3.2.2.1;Basic Constructs in Application Model;529
11.3.2.2.2;Other Constructs in the Application Model;533
11.3.2.3;Presentation Modeling;535
11.3.3;User Modeling;536
11.3.4;Aspect Orientation;537
11.3.5;Tool Support;539
11.3.5.1;Hera Studio;539
11.3.5.2;Other Tools;541
11.3.6;Related Work;542
11.3.6.1;WebML;543
11.3.6.2;OOHDM;543
11.3.6.3;UWE;544
11.3.6.4;OOWS;545
11.3.7;Conclusion;546
11.3.8;References;547
12;Index;549