Semantic Web Dog Food

Presented at: 7th International Semantic Web Conference (ISWC2008)

by Chaitanya Chemudugunta, America Holloway, Padhraic Smyth, Mark Steyvers

Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. The methodology we propose uses statistical topic models (also known as latent Dirichlet allocation models). We demonstrate the utility of this general framework in two ways. We first illustrate how the methodology can be used to automatically tag Web pages with concepts from a known set of concepts without any need for labeled documents. We then perform a series of experiments that quantify how combining human-defined semantic knowledge with data-driven techniques leads to better language models than can be obtained with either alone.

Keywords: ontologies, tagging, topic models, unsupervised learning, Semantic Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc/2008/paper/research/334

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