http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
A large number of information systems use many different individual schemas to represent data. Semantically linking these schemas is a necessary precondition to establish interoperability between agents and services. Consequently, ontology alignment and mapping for data integration has become central to building a world-wide semantic web. Ontology Alignment: Bridging the Semantic Gap introduces novel methods and approaches for semantic integration. In addition to developing new methods for ontology alignment, the author provides extensive explanations of up-to-date case studies. The topic of this book, coupled with the application-focused methodology, will appeal to professionals from a number of different domains. Designed for practitioners and researchers in industry, Ontology Alignment: Bridging the Semantic Web Gap is also suitable for advanced-level students in computer science and electrical engineering.
Introduction and Overview: Motivation, Contribution, Overview -- Foundations: Ontology, Ontology Alignment, Further Terms, Ontology Similarity, Use Cases, Requirements -- Related Work: Theory of Alignment, Existing Alignment Approaches -- Alignment Process: General Ontology Alignment Process, Alignment Approach, Process for Related Approaches, Evaluation -- Advanced Novel Methods: Efficiency, Machine Learning, Active Alignment, Adaptive Alignment, Integrated Approach -- Tools and Applications: Basic Infrastructure, Ontology Mapping Based on Axioms, Ontology Engineering Platform, Semantic Web and Peer-to-Peer (SWAP), Semantically Enabled Knowledge Technologies (SEKT) -- Next Steps: Generalization, Complex Alignments, Completeness of Alignments, Outlook -- Conclusion: Content Summary, Assessment of Contributions, Final Statements.
Information Systems Applications (incl. Internet)
Artificial Intelligence (incl. Robotics)
Information Systems and Communication Service
Semantic Web and Beyond, Computing for Human Experience, 4