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DAWAK
2006
Springer

Learning Classifiers from Distributed, Ontology-Extended Data Sources

10 years 5 months ago
Learning Classifiers from Distributed, Ontology-Extended Data Sources
Abstract. There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data sources in several increasingly data-rich application domains. In this paper, we precisely formulate and solve the problem of learning classifiers from such data sources, in a setting where each data source has a hierarchical ontology associated with it and semantic correspondences between data source ontologies and a user ontology are supplied. The proposed approach yields algorithms for learning a broad class of classifiers (including Bayesian networks, decision trees, etc.) from semantically heterogeneous distributed data with strong performance guarantees relative to their centralized counterparts. We illustrate the application of the proposed approach in the case of learning Naive Bayes classifiers from distributed, ontology-extended data sources.
Doina Caragea, Jun Zhang 0002, Jyotishman Pathak,
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where DAWAK
Authors Doina Caragea, Jun Zhang 0002, Jyotishman Pathak, Vasant Honavar
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