document: Automatically refining the wikipedia infobox ontology 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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