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We propose a new end-to-end question answering model, which learns toaggregate answer evidence from an incomplete knowledge base (KB) and a set ofretrieved text snippets. Under the assumptions that the structured KB is easierto query and the acquired knowledge can help the understanding of unstructuredtext, our model first accumulates knowledge of entities from a question-relatedKB subgraph; then reformulates the question in the latent space and reads thetexts with the accumulated entity knowledge at hand. The evidence from KB andtexts are finally aggregated to predict answers. On the widely-used KBQAbenchmark WebQSP, our model achieves consistent improvements across settingswith different extents of KB incompleteness.

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader