Recent advances in automatic knowledge acquisition methods make it possible to construct massive knowledge bases of semantic relations, containing information potentially unknown to their users. However for certain data mining tasks like finding potential causes of a disease or side-effects of a drug, where missing a small piece of information can have grave consequences, the coverage of automatically acquired knowledge bases is often insufficient. This paper explores the use of automatic hypothesis generation for expanding a knowledge base of semantic relations, using distributional word similarities obtained from a large Web corpus. If successful, such a method can drastically improve the coverage of automatically acquired semantic relations, at the expense of a slight reduction in accuracy. We show that large scale similarity-based relation expansion works quite well for this purpose. Using a 100 million Japanese Web page corpus as input, we could generate a substantial amount of new semantic relations that were not found in the input corpus but whose validity was confirmed in a much larger Web corpus, i.e., by using a commercial Web search engine.
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