Multi-hop knowledge base question answering with an iterative sequence matching model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

46 Scopus citations

Abstract

Knowledge Base Question Answering (KBQA) has attracted much attention and recently there has been more interest in multi-hop KBQA. In this paper, we propose a novel iterative sequence matching model to address several limitations of previous methods for multi-hop KBQA. Our method iteratively grows the candidate relation paths that may lead to answer entities. The method prunes away less relevant branches and incrementally assigns matching scores to the paths. Empirical results demonstrate that our method can significantly outperform existing methods on three different benchmark datasets.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages359-368
Number of pages10
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
Country/TerritoryChina
CityBeijing
Period8/11/1911/11/19

Keywords

  • Knowledge base question answering
  • Multi hop question answering
  • Sequence matching model

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