Entity Enabled Relation Linking

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

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

Fingerprint

Linking
Graph in graph theory
Question Answering
Natural Language
Baseline
Benchmark
Computing
Evaluation
Knowledge

Keywords

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

Cite this

Pan, J. Z., Zhang, M., Singh, K., van Harmelen, F., Gu, J., & Zhang, Z. (2019). Entity Enabled Relation Linking. In C. Ghidini, O. Hartig, M. Maleshkova, V. Svátek, I. Cruz, A. Hogan, J. Song, M. Lefrançois, ... F. Gandon (Eds.), The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings (Vol. 1, pp. 523-538). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11778 LNCS). Springer. https://doi.org/10.1007/978-3-030-30793-6_30
Pan, Jeff Z. ; Zhang, Mei ; Singh, Kuldeep ; van Harmelen, Frank ; Gu, Jinguang ; Zhang, Zhi. / Entity Enabled Relation Linking. The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings. editor / Chiara Ghidini ; Olaf Hartig ; Maria Maleshkova ; Vojtech Svátek ; Isabel Cruz ; Aidan Hogan ; Jie Song ; Maxime Lefrançois ; Fabien Gandon. Vol. 1 Springer, 2019. pp. 523-538 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Entity Enabled Relation Linking",
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.",
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author = "Pan, {Jeff Z.} and Mei Zhang and Kuldeep Singh and {van Harmelen}, Frank and Jinguang Gu and Zhi Zhang",
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editor = "Chiara Ghidini and Olaf Hartig and Maria Maleshkova and Vojtech Sv{\'a}tek and Isabel Cruz and Aidan Hogan and Jie Song and Maxime Lefran{\cc}ois and Fabien Gandon",
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Pan, JZ, Zhang, M, Singh, K, van Harmelen, F, Gu, J & Zhang, Z 2019, Entity Enabled Relation Linking. in C Ghidini, O Hartig, M Maleshkova, V Svátek, I Cruz, A Hogan, J Song, M Lefrançois & F Gandon (eds), The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings. vol. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11778 LNCS, Springer, pp. 523-538, 18th International Semantic Web Conference, ISWC 2019, Auckland, New Zealand, 26/10/19. https://doi.org/10.1007/978-3-030-30793-6_30

Entity Enabled Relation Linking. / Pan, Jeff Z.; Zhang, Mei; Singh, Kuldeep; van Harmelen, Frank; Gu, Jinguang; Zhang, Zhi.

The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings. ed. / Chiara Ghidini; Olaf Hartig; Maria Maleshkova; Vojtech Svátek; Isabel Cruz; Aidan Hogan; Jie Song; Maxime Lefrançois; Fabien Gandon. Vol. 1 Springer, 2019. p. 523-538 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11778 LNCS).

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

TY - GEN

T1 - Entity Enabled Relation Linking

AU - Pan, Jeff Z.

AU - Zhang, Mei

AU - Singh, Kuldeep

AU - van Harmelen, Frank

AU - Gu, Jinguang

AU - Zhang, Zhi

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Knowledge Graph

KW - Predicate linking

KW - Question answering

KW - Semantic search

KW - Semantic Web

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U2 - 10.1007/978-3-030-30793-6_30

DO - 10.1007/978-3-030-30793-6_30

M3 - Conference contribution

SN - 9783030307929

VL - 1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 523

EP - 538

BT - The Semantic Web – ISWC 2019

A2 - Ghidini, Chiara

A2 - Hartig, Olaf

A2 - Maleshkova, Maria

A2 - Svátek, Vojtech

A2 - Cruz, Isabel

A2 - Hogan, Aidan

A2 - Song, Jie

A2 - Lefrançois, Maxime

A2 - Gandon, Fabien

PB - Springer

ER -

Pan JZ, Zhang M, Singh K, van Harmelen F, Gu J, Zhang Z. Entity Enabled Relation Linking. In Ghidini C, Hartig O, Maleshkova M, Svátek V, Cruz I, Hogan A, Song J, Lefrançois M, Gandon F, editors, The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings. Vol. 1. Springer. 2019. p. 523-538. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-30793-6_30