Abstract: KB completion can be viewed as answering structured queries against a KB. Since answering the queries requires more than simply retrieving known facts, answering these queries requires some non-trivial processing, and hence is broadly similar to logical inference in a conventional symbolic KB. KB question-answering (KBQA) also answers queries against a KB, but in this case the queries are unstructured text queries. So both KBQA and KBC use some analog of "reasoning" over a KB. This raises the question: what can we learn about KBQA and KBC from the classical AI subfield of knowledge representation (KR)? In KR the central question is how to represent knowledge in a form that supports efficient, expressive reasoning. In my talk I will try to revisit this question in the context of modern neural learning methods, and tie the themes explored in classical KR to recently-proposed methods for KBC and KBQA.

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