Knowledge graph embedding based question answering

Xiao Huang, Jingyuan Zhang, Dingcheng Li, Ping Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

48 Citations (Scopus)

Abstract

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without knowing its data structures. QA-KG is a nontrivial problem since capturing the semantic meaning of natural language is difficult for a machine. Meanwhile, many knowledge graph embedding methods have been proposed. The key idea is to represent each predicate/entity as a low-dimensional vector, such that the relation information in the KG could be preserved. The learned vectors could benefit various applications such as KG completion and recommender systems. In this paper, we explore to use them to handle the QA-KG problem. However, this remains a challenging task since a predicate could be expressed in different ways in natural language questions. Also, the ambiguity of entity names and partial names makes the number of possible answers large. To bridge the gap, we propose an effective Knowledge Embedding based Question Answering (KEQA) framework. We focus on answering the most common types of questions, i.e., simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified. To answer a simple question, instead of inferring its head entity and predicate directly, KEQA targets at jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. Based on a carefully-designed joint distance metric, the three learned vectors' closest fact in the KG is returned as the answer. Experiments on a widely-adopted benchmark demonstrate that the proposed KEQA outperforms the state-of-the-art QA-KG methods.

Original languageEnglish
Title of host publicationWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages105-113
Number of pages9
ISBN (Electronic)9781450359405
DOIs
Publication statusPublished - 30 Jan 2019
Externally publishedYes
Event12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia
Duration: 11 Feb 201915 Feb 2019

Publication series

NameWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

Conference

Conference12th ACM International Conference on Web Search and Data Mining, WSDM 2019
CountryAustralia
CityMelbourne
Period11/02/1915/02/19

Keywords

  • Deep learning
  • Knowledge graph embedding
  • Question answering

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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