Ontology Managememt provides an up-to-date, scientifically correct, concise and easy-to-read reference on this topic. The book includes relevant tasks, practical and theoretical challenges, limitations and methodologies, plus available tooling support. The editors discuss integrating the conceptual and technical dimensions with a business view on using ontologies, stressing the cost dimension of ontology engineering and offering guidance on how to derive ontologies semi-automatically from existing standards and specifications.
The book covers a wide range of topics related to the Semantic Web, starting with invited talks that address the challenges and potential of this technology. It delves into large-scale shallow semantics and their implications for natural language processing (NLP), usability, and ontology alignment. Various methodologies for matching hierarchical classifications and ontologies are explored, including community-driven approaches and iterative algorithms that utilize training data.
The text discusses empirical merging of ontologies and proposes a universal uncertainty representation framework. It emphasizes ontology engineering, including the conversion of thesauri to SKOS and iterative case studies. Evaluation methods for ontologies are examined, along with benchmarks aimed at improving RDF(S) importers and exporters. The evolution of ontologies is addressed, focusing on repairing inconsistencies and winnowing based on application use.
Additionally, the book covers ontology learning, including automatic extraction of hierarchical relations and acquiring high-quality semantic metadata. It introduces effective integration of rules and reasoning, multi-viewpoint reasoning, and cognitive principles for visual ontology cleaning. Searching and querying are enhanced through tools like Beagle and RDFBroker, while semantic annotation techniques are discussed, including semi-automatic annotation environments.
The text also explores sem
