An Unsupervised Method for Ontology Population from the Web

Knowledge engineers have had difficulty in automatically constructing and populating domain ontologies, mainly due to the well-known knowledge acquisition bottleneck. In this paper, we attempt to alleviate this problem by proposing an iterative unsupervised approach to identifying and extracting ontological class instances from the Web. The proposed approach considers the Web as a big corpus and relies on a confidence-weighted metric based on semantic measures and web-scale statistics as types of evidence. Moreover, our iterative method is able to learn, to some extent, domain-specific linguistic patterns for extracting ontological class instances. We obtained encouraging results for the final ranking of candidate instances as well as an accuracy performance up to 97% for the patterns found by our method.

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