Soft Computing in Ontologies and Semantic Web [electronic resource] / edited by Zongmin Ma.

Probability in Ontologies and Semantic Web -- BayesOWL: Uncertainty Modeling in Semantic Web Ontologies -- Modeling Uncertainty in Semantic Web Taxonomies -- A Probabilistic, Logic-Based Framework for Automated Web Directory Alignment -- The SP Theory and the Representation and Processing of Knowledge -- Fuzzy Logic in Ontologies and Semantic Web -- Dynamic Services for Open Ambient Intelligence Systems -- Development of Ontologies by the Lowest Common Abstraction of Terms Using Fuzzy Hypernym Chains -- Beyond the Semantic Web: Fuzzy Logic-Based Web Intelligence -- An Ontology-Based Method for User Model Acquisition -- On Some Problems of Decision-Making Under Uncertainty in the Semantic Web -- Automatic Thematic Categorization of Multimedia Documents using Ontological Information and Fuzzy Algebra.

This book covers in a great depth the fast growing topic of tools, techniques and applications of soft computing (e.g., fuzzy logic, genetic algorithms, neural networks, rough sets, Bayesian networks, and other probabilistic techniques) in the ontologies and Semantic Web. How components of the Semantic Web (like the RDF, Description Logics, ontologies) can be covered with a soft computing focus is shown. The book aims to provide a single account of current studies in soft computing approaches to the ontologies and the Semantic Web. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of the Web intelligence, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.