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Automatically Refining the Wikipedia Infobox Ontology (2008)
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BibTeX
@MISC{Wu08automaticallyrefining,
author = {Fei Wu and Daniel S. Weld},
title = {Automatically Refining the Wikipedia Infobox Ontology },
year = {2008}
}
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Abstract
The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia’s infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia’s infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.
Keyphrases
wikipedia infobox ontology cleanly-structured ontology ontology refinement full power wikipedia infobox-class schema human volunteer wikipedia infoboxes powerful joint-inference approach semantic knowledge improved query processing wikipedia infobox-class ontology markov logic network combined effort machine learning problem training data natural language text autonomous system numerous fact present experiment joint-inference approach machine-harvestable object-attribute-value triple rich ontology
