2008 AutomaticallyRefTheWikipedIboxOnt

Subject Headings: Kylin Ontology Generator, Wikipedia Infobox, Ontology, Ontology Update Task, Markov Logic Network.

Notes

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 AutomaticallyRefTheWikipedIboxOnt	Fei Wu Daniel S. Weld			Automatically Refining the Wikipedia Infobox Ontology		Proceeding of the 17th International Conference on World Wide Web	http://turing.cs.washington.edu/papers/www08.pdf	10.1145/1367497.1367583		2008