Learning Link-Based Naïve Bayes Classifiers from Ontology-Extended Distributed Data

Authors:
Cornelia Caragea;Doina Caragea;Vasant Honavar
Affiliations:
Computer Science Department, Iowa State University,;Computer and Information Sciences, Kansas State University,;Computer Science Department, Iowa State University,
Venue:
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part II
Year:
2009

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Abstract

We address the problem of learning predictive models from multiple large, distributed, autonomous, and hence almost invariably semantically disparate, relational data sources from a user's point of view. We show under fairly general assumptions, how to exploit data sources annotated with relevant meta data in building predictive models (e.g., classifiers) from a collection of distributed relational data sources, without the need for a centralized data warehouse, while offering strong guarantees of exactness of the learned classifiers relative to their centralized relational learning counterparts. We demonstrate an application of the proposed approach in the case of learning link-based Naïve Bayes classifiers and present results of experiments on a text classification task that demonstrate the feasibility of the proposed approach.