Automatically Refining the Wikipedia Infobox Ontology Fei Wu Daniel S. Weld Computer Science & Engineering Department, Computer Science & Engineering Department, University of Washington, Seattle, WA, USA University of Washington, Seattle, WA, USA
[email protected] [email protected] ABSTRACT example, our autonomous Kylin system [35] trained machine-learning algorithms on the infobox data, yielding extractors which can accu- The combined efforts of human volunteers have recently extracted 2 numerous facts from Wikipedia, storing them as machine-harvestable rately generate infoboxes for articles which don’t yet have them. object-attribute-value triples in Wikipedia infoboxes. Machine learn- We estimate that this approach can add over 10 million additional ing systems, such as Kylin, use these infoboxes as training data, facts to those already incorporated into DBpedia. By running the accurately extracting even more semantic knowledge from natural learned extractors on a wider range of Web text and validating with language text. But in order to realize the full power of this informa- statistical tests (as pioneered in the KnowItAll system [16]), one tion, it must be situated in a cleanly-structured ontology. This paper could gather even more structured data. introduces KOG, an autonomous system for refining Wikipedia’s In order to effectively exploit extracted data, however, the triples infobox-class ontology towards this end. We cast the problem of must be organized using a clean and consistent ontology. Unfortu- ontology refinement as a machine learning problem and solve it nately, while Wikipedia has a category system for articles, the facil- using both SVMs and a more powerful joint-inference approach ity is noisy, redundant, incomplete, inconsistent and of very limited expressed in Markov Logic Networks.