Scalable Cleanup of Information Extraction Data Using Ontologies

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A paper written by Julian Dolby, Li Ma, Aaron Kershenbaum, Aditya Kalyanpur, James Fan, Kavitha Srinivas, J. William Murdock, Christopher Welty and Achille Fokoue. It was presented at the ISWC2007+ASWC2007.

[edit] Abstract

The approach of using ontology reasoning to cleanse the output of information extraction tools was first articulated in SemantiClean. A limiting factor in applying this approach has been that ontology reasoning to find inconsistencies does not scale to the size of data produced by information extraction tools. In this paper, we describe techniques to scale inconsistency detection, and illustrate the use of our techniques to produce a consistent subset of a knowledge base with several thousand inconsistencies.

A linked list of all papers is provided in the article on ISWC2007+ASWC2007 papers. This article has originally been created from the ISWC 2007/ASWC 2007 metadata.

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