Muutke küpsiste eelistusi

E-raamat: Semantic Web Technologies - Trends and Research in Ontology-based Systems: Trends and Research in Ontology-based Systems [Wiley Online]

Edited by (British Telecommunications plc, UK), Edited by (University of Karlsruhe, Germany), Edited by (British Telecommunications plc, UK)
  • Formaat: 328 pages
  • Ilmumisaeg: 21-Apr-2006
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 047003033X
  • ISBN-13: 9780470030332
Teised raamatud teemal:
  • Wiley Online
  • Hind: 158,59 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Semantic Web Technologies - Trends and Research in Ontology-based Systems: Trends and Research in Ontology-based SystemsSuurem pilt
  • Formaat: 328 pages
  • Ilmumisaeg: 21-Apr-2006
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 047003033X
  • ISBN-13: 9780470030332
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/047003033X
The Semantic Web combines the descriptive languages RDF and OWL with the data-centric language XML to provide machine-interpretable descriptions of the content of Web documents. This work overviews key semantic knowledge technologies and research. It explains (semi-) automatic ontology generation and metadata extraction in depth, and covers ontology management and mediation. Theoretical concepts are illustrated with three case studies of industrial applications in digital libraries, the legal sector, and the telecommunication industry. The audience for the book includes graduate and advanced undergraduate students, and academic and industrial researchers. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)

The Semantic Web combines the descriptive languages RDF (Resource Description Framework) and OWL (Web Ontology Language), with the data-centric, customizable XML (eXtensible Mark-up Language) to provide descriptions of the content of Web documents. These machine-interpretable descriptions allow more intelligent software systems to be written, automating the analysis and exploitation of web-based information.

Software agents will be able to create automatically new services from already published services, with potentially huge implications for models of e-Business.

Semantic Web Technologies provides a comprehensive overview of key semantic knowledge technologies and research.   The authors explain (semi-)automatic ontology generation and metadata extraction in depth, along with ontology management and mediation. Further chapters examine how Semantic Web technology is being applied in knowledge management (“Semantic Information Access”) and in the next generation of Web services.

Semantic Web Technologies:

  • Provides a comprehensive exposition of the state-of-the art in Semantic Web research and key technologies.
  • Explains the use of ontologies and metadata to achieve machine-interpretability.
  • Describes methods for ontology learning and metadata generation.
  • Discusses ontology management and evolution, covering ontology change detection and propagation, ontology dependency and mediation.
  • Illustrates the theoretical concepts with three case studies on industrial applications in digital libraries, the legal sector and the telecommunication industry.

Graduate and advanced undergraduate students, academic and industrial researchers in the field will all find Semantic Web Technologies an essential guide to the technologies of the Semantic Web.

Foreword xi
1. Introduction
1(8)
1.1. Semantic Web Technologies
1(1)
1.2. The Goal of the Semantic Web
2(2)
1.3. Ontologies and Ontology Languages
4(1)
1.4. Creating and Managing Ontologies
5(1)
1.5. Using Ontologies
6(1)
1.6. Applications
7(1)
1.7. Developing the Semantic Web
8(1)
References
8(1)
2. Knowledge Discovery for Ontology Construction
9(20)
2.1. Introduction
9(1)
2.2. Knowledge Discovery
10(1)
2.3. Ontology Definition
10(1)
2.4. Methodology for Semi-automatic Ontology Construction
11(1)
2.5. Ontology Learning Scenarios
12(1)
2.6. Using Knowledge Discovery for Ontology Learning
13(9)
2.6.1. Unsupervised Learning
14(2)
2.6.2. Semi-Supervised, Supervised, and Active Learning
16(2)
2.6.3. Stream Mining and Web Mining
18(1)
2.6.4. Focused Crawling
18(1)
2.6.5. Data Visualization
19(3)
2.7. Related Work on Ontology Construction
22(2)
2.8. Discussion and Conclusion
24(1)
Acknowledgments
24(1)
References
25(4)
3. Semantic Annotation and Human Language Technology
29(22)
3.1. Introduction
29(2)
3.2. Information Extraction: A Brief Introduction
31(4)
3.2.1. Five Types of IE
32(1)
3.2.2. Entities
33(1)
3.2.3. Mentions
33(1)
3.2.4. Descriptions
34(1)
3.2.5. Relations
34(1)
3.2.6. Events
34(1)
3.3. Semantic Annotation
35(2)
3.3.1. What is Ontology-Based Information Extraction
36(1)
3.4. Applying 'Traditional' IE in Semantic Web Applications
37(3)
3.4.1. AeroDAML
38(1)
3.4.2. Amilcare
38(1)
3.4.3. MnM
39(1)
3.4.4. S-Cream
39(1)
3.4.5. Discussion
40(1)
3.5. Ontology-based IE
40(5)
3.5.1. Magpie
40(1)
3.5.2. Pankow
41(1)
3.5.3. SemTag
41(1)
3.5.4. Kim
42(1)
3.5.5. KIM Front-ends
43(2)
3.6. Deterministic Ontology Authoring using Controlled Language IE
45(3)
3.7. Conclusion
48(1)
References
49(2)
4. Ontology Evolution
51(20)
4.1. Introduction
51(1)
4.2. Ontology Evolution: State-of-the-art
52(8)
4.2.1. Change Capturing
53(1)
4.2.2. Change Representation
54(2)
4.2.3. Semantics of Change
56(2)
4.2.4. Change Propagation
58(1)
4.2.5. Change Implementation
59(1)
4.2.6. Change Validation
60(1)
4.3. Logical Architecture
60(2)
4.4. Data-driven Ontology Changes
62(4)
4.4.1. Incremental Ontology Learning
64(2)
4.5. Usage-driven Ontology Changes
66(2)
4.5.1. Usage-driven Hierarchy Pruning
67(1)
4.6. Conclusion
68(1)
References
69(2)
5. Reasoning With Inconsistent Ontologies: Framework, Prototype, and Experiment 71(68)
5.1. Introduction
71(2)
5.2. Brief Survey of Approaches to Reasoning with Inconsistency
73(2)
5.2.1. Paraconsistent Logics
73(1)
5.2.2. Ontology Diagnosis
74(1)
5.2.3. Belief Revision
74(1)
5.2.4. Synthesis
75(1)
5.3. Brief Survey of Causes for Inconsistency in the Semantic Web
75(4)
5.3.1. Inconsistency by Mis-representation of Default
75(2)
5.3.2. Inconsistency Caused by Polysemy
77(1)
5.3.3. Inconsistency through Migration from Another Formalism
77(1)
5.3.4. Inconsistency Caused by Multiple Sources
78(1)
5.4. Reasoning with Inconsistent Ontologies
79(3)
5.4.1. Inconsistency Detection
79(1)
5.4.2. Formal Definitions
80(2)
5.5. Selection Functions
82(1)
5.6. Strategies for Selection Functions
83(2)
5.7. Syntactic Relevance-Based Selection Functions
85(2)
5.8. Prototype of Pion
87(4)
5.8.1. Implementation
87(1)
5.8.2. Experiments and Evaluation
88(3)
5.8.3. Future Experiments
91(1)
5.9. Discussion and Conclusions
91(1)
Acknowledgment
92(1)
References
92(3)
6. Ontology Mediation, Merging, and Aligning
95(20)
6.1. Introduction
95(1)
6.2. Approaches in Ontology Mediation
96(8)
6.2.1. Ontology Mismatches
97(1)
6.2.2. Ontology Mapping
97(3)
6.2.3. Ontology Alignment
100(2)
6.2.4. Ontology Merging
102(2)
6.3. Mapping and Querying Disparate Knowledge Bases
104(7)
6.3.1. Mapping Language
106(2)
6.3.2. A (Semi-)Automatic Process for Ontology Alignment
108(2)
6.3.3. OntoMap: an Ontology Mapping Tool
110(1)
6.4. Summary
111(1)
References
112(3)
7. Ontologies for Knowledge Management
115(24)
7.1. Introduction
115(1)
7.2. Ontology Usage Scenario
116(1)
7.3. Terminology
117(6)
7.3.1. Data Qualia
119(1)
7.3.2. Sorts of Data
120(3)
7.4. Ontologies as RDBMS Schema
123(1)
7.5. Topic-ontologies Versus Schema-ontologies
124(2)
7.6. Proton Ontology
126(9)
7.6.1. Design Rationales
126(1)
7.6.2. Basic Structure
127(1)
7.6.3. Scope, Coverage, Compliance
128(2)
7.6.4. The Architecture of Proton
130(1)
7.6.5. Topics in Proton
131(2)
7.6.6. Proton Knowledge Management Module
133(2)
7.7. Conclusion
135(1)
References
136(3)
8. Semantic Information Access 139(52)
8.1. Introduction
139(1)
8.2. Knowledge Access and the Semantic WEB
139(13)
8.2.1. Limitations of Current Search Technology
140(2)
8.2.2. Role of Semantic Technology
142(1)
8.2.3. Searching XML
143(1)
8.2.4. Searching RDF
144(2)
8.2.5. Exploiting Domain-specific Knowledge
146(4)
8.2.6. Searching for Semantic Web Resources
150(1)
8.2.7. Semantic Browsing
151(1)
8.3. Natural Language Generation from Ontologies
152(4)
8.3.1. Generation from Taxonomies
153(1)
8.3.2. Generation of Interactive Information Sheets
154(1)
8.3.3. Ontology Verbalisers
154(1)
8.3.4. Ontogeneration
154(1)
8.3.5. Ontosum and Miakt Summary Generators
155(1)
8.4. Device Independence: Information Anywhere
156(8)
8.4.1. Issues in Device Independence
157(3)
8.4.2. Device Independence Architectures and Technologies
160(2)
8.4.3. DIWAF
162(2)
8.5. SEKTAgent
164(2)
8.6. Concluding Remarks
166(1)
References
167(4)
9. Ontology Engineering Methodologies
171(20)
9.1. Introduction
171(1)
9.2. The Methodology Focus
172(2)
9.2.1. Definition of Methodology for Ontologies
172(1)
9.2.2. Methodology
173(1)
9.2.3. Documentation
174(1)
9.2.4. Evaluation
174(1)
9.3. Past and Current Research
174(6)
9.3.1. Methodologies
174(3)
9.3.2. Ontology Engineering Tools
177(1)
9.3.3. Discussion and Open Issues
178(2)
9.4. Diligent Methodology
180(5)
9.4.1. Process
180(3)
9.4.2. Argumentation Support
183(2)
9.5. First Lessons Learned
185(1)
9.6. Conclusion and Next Steps
186(1)
References
187(4)
10. Semantic Web Services – Approaches and Perspectives 191(46)
10.1. Semantic Web Services – A Short Overview
191(1)
10.2. The WSMO Approach
192(15)
10.2.1. The Conceptual Model – The Web Services Modeling Ontology (WSMO)
193(5)
10.2.2. The Language – The Web Service Modeling Language (WSML)
198(6)
10.2.3. The Execution Environment – The Web Service Modeling Execution Environment (WSMX)
204(3)
10.3. The OWL-S Approach
207(6)
10.3.1. OWL-S Service Profiles
209(1)
10.3.2. OWL-S Service Models
210(3)
10.4. The SWSF Approach
213(5)
10.4.1. The Semantic Web Services Ontology (SWSO)
213(3)
10.4.2. The Semantic Web Services Language (SWSL)
216(2)
10.5. The IRS-III Approach
218(4)
10.5.1. Principles Underlying IRS-III
218(2)
10.5.2. The IRS-III Architecture
220(1)
10.5.3. Extension to WSMO
221(1)
10.6. The WSDL-S Approach
222(4)
10.6.1. Aims and Principles
222(2)
10.6.2. Semantic Annotations
224(2)
10.7. Semantic Web Services Grounding: The Link Between SWS and Existing Web Services Standards
226(6)
10.7.1. General Grounding Uses and Issues
226(2)
10.7.2. Data Grounding
228(2)
10.7.3. Behavioural Grounding
230(2)
10.8. Conclusions and Outlook
232(2)
References
234(3)
11. Applying Semantic Technology to a Digital Library 237(22)
11.1. Introduction
237(1)
11.2. Digital Libraries: The State-of-the-art
238(4)
11.2.1. Working Libraries
238(1)
11.2.2. Challenges
239(2)
11.2.3. The Research Environment
241(1)
11.3. A Case Study: The BT Digital Library
242(6)
11.3.1. The Starting Point
242(2)
11.3.2. Enhancing the Library with Semantic Technology
244(4)
11.4. The Users' View
248(2)
11.5. Implementing Semantic Technology in a Digital Library
250(5)
11.5.1. Ontology Engineering
250(1)
11.5.2. BT Digital Library End-user Applications
251(1)
11.5.3. The BT Digital Library Architecture
252(3)
11.5.4. Deployment View of the BT Digital Library
255(1)
11.6. Future Directions
255(2)
References
257(2)
12. Semantic Web: A Legal Case Study 259(22)
12.1. Introduction
259(1)
12.2. Profile of the Users
260(2)
12.3. Ontologies for Legal Knowledge
262(10)
12.3.1. Legal Ontologies: State of the Art
263(2)
12.3.2 Ontologics of Professional Knowledge: OPJK
265(2)
12.3.3. Benefits of Semantic Technology and Methodology
267(5)
12.4. Architecture
272(6)
12.4.1. Iuriservice Prototype
272(6)
12.5. Conclusions
278(1)
References
278(3)
13. A Semantic Service-Oriented Architecture for the Telecommunications Industry 281(20)
13.1. Introduction
281(1)
13.2. Introduction to Service-oriented Architectures
282(2)
13.3. A Semantic Service-orientated architecture
284(2)
13.4. Semantic Mediation
286(1)
13.4.1. Data Mediation
287(1)
13.4.2. Process Mediation
287(1)
13.5. Standards and Ontologies in Telecommunications
287(3)
13.5.1. eTOM
289(1)
13.5.2. SID
289(1)
13.5.3. Adding Semantics
290(1)
13.6. Case Study
290(8)
13.6.1. Broadband Diagnostics
292(1)
13.6.2. The B2B Gateway Architecture
292(2)
13.6.3. Semantic B2B Integration Prototype
294(3)
13.6.4. Prototype Implementation
297(1)
13.7. Conclusion
298(1)
References
299(2)
14. Conclusion arid Outlook 301(8)
14.1. Management of Networked Ontologies
301(1)
14.2. Engineering of Networked Ontologies
302(1)
14.3. Contextualizing Ontologies
303(1)
14.4. Cross Media Resources
304(2)
14.5. Social Semantic Desktop
306(1)
14.6. Applications
307(2)
Index 309


Dr John Davies leads the Next Generation Web research group at BT. Current interests focus on the application of semantic web technology to knowledge management and semantic web services. John is industrial chair of the Semantic Web Services Initiative, co-organiser of the European Semantic Web Conference series and Project Director of the SEKT EU integrated project (Semantically-Enabled Knowledge Technologies).  He has written and edited many papers and books in related areas. Rudi Studer is Professor at Institute of Applied Informatics and Formal Description Methods, University of Karlsruhe.  His research spans the fields of business intelligence, e-learning, knowledge discovery and management, ontology-based knowledge management systems and the semantic web.  He has authored numerous journal and conference papers on these topics.

Paul Warren works in BT's Next Generation Web research group, where he is SEKT project manager and also responsible for the project's exploitation strategy.   Paul has published widely on technology management, technology foresight, and recently the application of the Semantic Web.
Püsilink: https://www.kriso.ee/db/9780470030332_pe.html
Märksõnad: