Proceedings of the First International Workshop on Formalisms and Methodology for Learning by Reading

Unsupervised techniques for discovering ontology elements from Wikipedia article links


We present an unsupervised and unrestricted approach to discovering an infobox like ontology by exploiting the inter-article links within Wikipedia. It discovers new slots and fillers that may not be available in the Wikipedia infoboxes. Our results demonstrate that there are certain types of properties that are evident in the link structure of resources like Wikipedia that can be predicted with high accuracy using little or no linguistic analysis. The discovered properties can be further used to discover a class hierarchy. Our experiments have focused on analyzing people in Wikipedia, but the techniques can be directly applied to other types of entities in text resources that are rich with hyperlinks.

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learning, natural language processing, wikipedia


Association for Computational Linguistics

(held in conjunction with NAACL)

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