Entity Enabled Relation Linking

Jeff Z. Pan*, Mei Zhang, Kuldeep Singh, Frank van Harmelen, Jinguang Gu, Zhi Zhang

*Corresponding author for this work

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

Abstract

Relation linking is an important problem for knowledge graph-based Question Answering. Given a natural language question and a knowledge graph, the task is to identify relevant relations from the given knowledge graph. Since existing techniques for entity extraction and linking are more stable compared to relation linking, our idea is to exploit entities extracted from the question to support relation linking. In this paper, we propose a novel approach, based on DBpedia entities, for computing relation candidates. We have empirically evaluated our approach on different standard benchmarks. Our evaluation shows that our approach significantly outperforms existing baseline systems in both recall, precision and runtime.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2019
Subtitle of host publication18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings
EditorsChiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon
PublisherSpringer
Pages523-538
Number of pages16
Volume1
ISBN (Electronic)9783030307936
ISBN (Print)9783030307929
DOIs
Publication statusPublished - 2019
Event18th International Semantic Web Conference, ISWC 2019 - Auckland, New Zealand
Duration: 26 Oct 201930 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11778 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Semantic Web Conference, ISWC 2019
CountryNew Zealand
CityAuckland
Period26/10/1930/10/19

Keywords

  • Knowledge Graph
  • Predicate linking
  • Question answering
  • Semantic search
  • Semantic Web

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