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Full Description
This is the first book to offer a cohesive treatment of the research problems in collaborative knowledge acquisition from semantically disparate information sources & approaches for addressing the problems. The book discusses the fundamental 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-contained survey will be a valuable reference tool.
Contents
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 Specified Data.- Steps Toward a Collaborative Knowledge Acquisition Environment.- Summary and Discussion
