Collaborative knowledge acquisition from semantically disparate, distributed data sources

This is the first book to offer a cohesive treatment of the research problemsin collaborative knowledge acquisition from semantically disparate information sources & approaches for addressing the problems. The book discusses thefundamental advances in this area covering a broad range & complexity of research issues. The approach taken incorporates a synergistic synthesis of insights, algorithms & results drawn from multiple areas including: Artificial Intelligence – especially machine learning, data mining, knowledge representation & inference, intelligent agents & multi-agent systems; Information Systems – especially databases, information integration, semantic web; & Distributed computing & software engineering (e.g. service-oriented computing). Written for researchers & graduate students as well as advanced practitioners in data mining, semantic technologies, AI, Information integration, the semantic web, & information systems, this accessible self-containedsurvey will be a valuable reference tool." “The first book to offer: Comprehensive treatment of the theoretical framework, algorithms & applications involving knowledge acquisition from large distributed autonomous data repositories Treatment of the theoretical framework for integration of information, from a user’s point of view Theoretically well-founded algorithms for learning classifiers from semantically heterogeneous information sources INDICE: Introduction.- Learning Predictive Models from Data Revisited.- Learning Predictive Models from Distributed Data.- Self-Describing Data Sources and Programs.- Bridging the Semantic Gap.- Learning Predictive Models from Semantically Disparate Data.- Learning Predictive Models from Partially SpecifiedData.- Steps Toward a Collaborative Knowledge Acquisition Environment.- Summary and Discussion

• ISBN: 978-1-84628-894-4
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 240
• Fecha Publicación: 01/05/2010
• Nº Volúmenes: 1
• Idioma: Inglés