Collaborative Knowledge Acquisition from Semantically Disparate, Distributed Data Sources von Vasant/Caragea Honavar

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 & multiagent systems; Information Systems especially databases, information integration, semantic web; & Distributed computing & software engineering (e.g. serviceoriented 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.

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