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Answering natural-language questions may often involve identifying hidden associations and implicit relationships. In some cases, an explicit question is asked by the user to discover some hidden concept related to a set of entities. Answering the explicit question and identifying the implicit entity both require the system to discover the semantically related but hidden concepts in the question. In this paper, we describe a spreading-activation approach to concept expansion, backed by three distinct knowledge resources for measuring semantic relatedness. We discuss how our spreading-activation approach is applied to address these questions, exemplified in Jeopardy!™ by questions in the “COMMON BONDS” category and by many Final Jeopardy! questions. We demonstrate the effectiveness of the approach by measuring its impact on IBM Watson™ performance on these questions.
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