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Structured query construction via knowledge graph embedding

  • Ruijie Wang
  • Meng WangEmail author
  • Jun Liu
  • Michael Cochez
  • Stefan Decker
Regular Paper
  • 13 Downloads

Abstract

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.

Keywords

Knowledge graph Query construction Knowledge graph embedding Natural language question answering 

Notes

Acknowledgements

This work is supported by National Key Research and Development Program of China (No. 2018YFB1004500), National Natural Science Foundation of China (61532015, 61532004, 61672419, and 61672418), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Centre for Engineering Science and Technology, Science and Technology Planning Project of Guangdong Province (No. 2017A010101029), Teaching Reform Project of XJTU (No. 17ZX044), and China Scholarship Council (No. 201806280450). We would like to express our gratitude to Mr. Zhouguo Chen for his advice during paper writing and experiments. The current work is an extension and continuation of our previous work that has been published in a conference paper of ICBK 2018 [40].

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Engineering Lab for Big Data AnalyticsXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  4. 4.Guang Dong Xi’an Jiaotong University AcademyShundeChina
  5. 5.VU AmsterdamAmsterdamThe Netherlands
  6. 6.Fraunhofer FITSankt AugustinGermany
  7. 7.Informatik 5RWTH Aachen UniversityAachenGermany
  8. 8.Faculty of Information TechnologyUniversity of JyväskyläJyvaskylaFinland

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