On Integrating Knowledge Graph Embedding into SPARQL Query Processing

Hyunjoong Kang, Sanghyun Hong, Kookjin Lee, Noseong Park, Soonhyun Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

SPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages371-374
Number of pages4
ISBN (Print)9781538672471
DOIs
StatePublished - Sep 5 2018
Externally publishedYes
Event25th IEEE International Conference on Web Services, ICWS 2018 - San Francisco, United States
Duration: Jul 2 2018Jul 7 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services

Conference

Conference25th IEEE International Conference on Web Services, ICWS 2018
Country/TerritoryUnited States
CitySan Francisco
Period7/2/187/7/18

Keywords

  • Knowledge Graph Embedding
  • Nearest Neighbor Searching
  • SPARQL Query Processing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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