A Joint Model for Question Answering over Multiple Knowledge Bases

Authors

  • Yuanzhe Zhang Institute of Automation, Chinese Academy of Sciences
  • Shizhu He Institute of Automation, Chinese Academy of Sciences
  • Kang Liu Institute of Automation, Chinese Academy of Sciences
  • Jun Zhao Institute of Automation, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v30i1.10381

Abstract

As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.

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Published

2016-03-05

How to Cite

Zhang, Y., He, S., Liu, K., & Zhao, J. (2016). A Joint Model for Question Answering over Multiple Knowledge Bases. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10381

Issue

Section

Technical Papers: NLP and Text Mining