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#Learning to Answer Complex Questions over Knowledge Bases with Query Composition.

  • author:Nikita Bhutani, Xinyi Zheng, H. V. Jagadish
  • abstract:Recent years have seen a surge of knowledge-based question answering (KB-QA) systems which provide crisp answers to user-issued questions by translating them to precise structured queries over a knowledge base (KB). A major challenge in KB-QA is bridging the gap between natural language expressions and the complex schema of the KB. As a result, existing methods focus on simple questions answerable with one main relation path in the KB and struggle with complex questions that require joining multiple relations. We propose a KB-QA system, TextRay, which answers complex questions using a novel decompose-execute-join approach. It constructs complex query patterns using a set of simple queries. It uses a semantic matching model which is able to learn simple queries using implicit supervision from question-answer pairs, thus eliminating the need for complex query patterns. Our proposed system significantly outperforms existing KB-QA systems on complex questions while achieving comparable results on simple questions.
  • keywords:complex questions, neural networks, question answering
  • interpretation:
  • pdf: paper
  • code:
  • dataset: CompQWeb,WebQSP
  • ppt/video:
  • curator: Wu Bo