LEADER 05278nam 22006254a 450 001 9910143584903321 005 20170809164629.0 010 $a1-280-44888-1 010 $a9786610448883 010 $a0-470-03033-X 010 $a0-470-03034-8 035 $a(CKB)1000000000357157 035 $a(EBL)257691 035 $a(OCoLC)475974274 035 $a(SSID)ssj0000243958 035 $a(PQKBManifestationID)11228750 035 $a(PQKBTitleCode)TC0000243958 035 $a(PQKBWorkID)10164958 035 $a(PQKB)10160555 035 $a(MiAaPQ)EBC257691 035 $a(EXLCZ)991000000000357157 100 $a20060223d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSemantic Web technologies$b[electronic resource] $etrends and research in ontology-based systems /$fJohn Davies, Rudi Studer, Paul Warren 210 $aChichester, England ;$aHoboken, NJ $cJohn Wiley & Sons$dc2006 215 $a1 online resource (328 p.) 300 $aDescription based upon print version of record. 311 $a0-470-02596-4 320 $aIncludes bibliographical references and index. 327 $aSemantic Web Technologies; Contents; Foreword; 1. Introduction; 1.1. Semantic Web Technologies; 1.2. The Goal of the Semantic Web; 1.3. Ontologies and Ontology Languages; 1.4. Creating and Managing Ontologies; 1.5. Using Ontologies; 1.6. Applications; 1.7. Developing the Semantic Web; References; 2. Knowledge Discovery for Ontology Construction; 2.1. Introduction; 2.2. Knowledge Discovery; 2.3. Ontology Definition; 2.4. Methodology for Semi-automatic Ontology Construction; 2.5. Ontology Learning Scenarios; 2.6. Using Knowledge Discovery for Ontology Learning; 2.6.1. Unsupervised Learning 327 $a2.6.2. Semi-Supervised, Supervised, and Active Learning2.6.3. Stream Mining and Web Mining; 2.6.4. Focused Crawling; 2.6.5. Data Visualization; 2.7. Related Work on Ontology Construction; 2.8. Discussion and Conclusion; Acknowledgments; References; 3. Semantic Annotation and Human Language Technology; 3.1. Introduction; 3.2. Information Extraction: A Brief Introduction; 3.2.1. Five Types of IE; 3.2.2. Entities; 3.2.3. Mentions; 3.2.4. Descriptions; 3.2.5. Relations; 3.2.6. Events; 3.3. Semantic Annotation; 3.3.1. What is Ontology-Based Information Extraction 327 $a3.4. Applying 'Traditional' IE in Semantic Web Applications3.4.1. AeroDAML; 3.4.2. Amilcare; 3.4.3. MnM; 3.4.4. S-Cream; 3.4.5. Discussion; 3.5. Ontology-based IE; 3.5.1. Magpie; 3.5.2. Pankow; 3.5.3. SemTag; 3.5.4. Kim; 3.5.5. KIM Front-ends; 3.6. Deterministic Ontology Authoring using Controlled Language IE; 3.7. Conclusion; References; 4. Ontology Evolution; 4.1. Introduction; 4.2. Ontology Evolution: State-of-the-art; 4.2.1. Change Capturing; 4.2.2. Change Representation; 4.2.3. Semantics of Change; 4.2.4. Change Propagation; 4.2.5. Change Implementation; 4.2.6. Change Validation 327 $a4.3. Logical Architecture4.4. Data-driven Ontology Changes; 4.4.1. Incremental Ontology Learning; 4.5. Usage-driven Ontology Changes; 4.5.1. Usage-driven Hierarchy Pruning; 4.6. Conclusion; References; 5. Reasoning With Inconsistent Ontologies: Framework, Prototype, and Experiment; 5.1. Introduction; 5.2. Brief Survey of Approaches to Reasoning with Inconsistency; 5.2.1. Paraconsistent Logics; 5.2.2. Ontology Diagnosis; 5.2.3. Belief Revision; 5.2.4. Synthesis; 5.3. Brief Survey of Causes for Inconsistency in the Semantic Web; 5.3.1. Inconsistency by Mis-representation of Default 327 $a5.3.2. Inconsistency Caused by Polysemy5.3.3. Inconsistency through Migration from Another Formalism; 5.3.4. Inconsistency Caused by Multiple Sources; 5.4. Reasoning with Inconsistent Ontologies; 5.4.1. Inconsistency Detection; 5.4.2. Formal Definitions; 5.5. Selection Functions; 5.6. Strategies for Selection Functions; 5.7. Syntactic Relevance-Based Selection Functions; 5.8. Prototype of Pion; 5.8.1. Implementation; 5.8.2. Experiments and Evaluation; 5.8.3. Future Experiments; 5.9. Discussion and Conclusions; Acknowledgment; References; 6. Ontology Mediation, Merging, and Aligning 327 $a6.1. Introduction 330 $aThe 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 T 606 $aSemantic Web 608 $aElectronic books. 615 0$aSemantic Web. 676 $a004.678 676 $a025.04 700 $aDavies$b J$g(N. John)$0895859 701 $aStuder$b Rudi$0895860 701 $aWarren$b Paul$g(Paul W.)$0895861 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143584903321 996 $aSemantic Web technologies$92001428 997 $aUNINA