research-article

Learning relatedness measures for entity linking

Authors:

Diego Ceccarelli,

Claudio Lucchese,

Salvatore Orlando,

Raffaele Perego,

Salvatore TraniAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 139 - 148

https://doi.org/10.1145/2505515.2505711

Published: 27 October 2013 Publication History

Abstract

Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowledge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant entities selected for annotation, since this minimizes errors in disambiguating entity-linking.

The definition of an effective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of different state-of-the-art entity-linking algorithms.

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Cited By

Feher DIbrahim FCheng ZSchlegel VMaidment TBagshaw JBatista-Navarro R(2023)Few-shot entity linking of food namesInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346360:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103463
Sevgili ÖShelmanov AArkhipov MPanchenko ABiemann CAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)Neural entity linkingSemantic Web10.3233/SW-22298613:3(527-570)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/SW-222986
Li MXing YKong FZhou G(2022)Towards better entity linkingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-020-0192-916:2Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11704-020-0192-9
Show More Cited By

Index Terms

Learning relatedness measures for entity linking
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

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cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

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Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Feher DIbrahim FCheng ZSchlegel VMaidment TBagshaw JBatista-Navarro R(2023)Few-shot entity linking of food namesInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346360:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103463
Sevgili ÖShelmanov AArkhipov MPanchenko ABiemann CAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)Neural entity linkingSemantic Web10.3233/SW-22298613:3(527-570)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/SW-222986
Li MXing YKong FZhou G(2022)Towards better entity linkingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-020-0192-916:2Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11704-020-0192-9
Ponza MCeccarelli DFerragina PMeij EKothari SLewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)Contextualizing Trending Entities in News StoriesProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441765(346-354)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441765
Hosseini HBagheri E(2021)Learning to rank implicit entities on TwitterInformation Processing & Management10.1016/j.ipm.2021.10250358:3(102503)Online publication date: May-2021
https://doi.org/10.1016/j.ipm.2021.102503
Linhares Pontes EMoreno JDoucet AHuang RWu DMarchionini GHe DCunningham SHansen P(2020)Linking Named Entities across Languages using Multilingual Word EmbeddingsProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398597(329-332)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1145/3383583.3398597
Al-Moslmi TGallofre Ocana ML. Opdahl AVeres C(2020)Named Entity Extraction for Knowledge Graphs: A Literature OverviewIEEE Access10.1109/ACCESS.2020.29739288(32862-32881)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973928
El Vaigh CGoasdoué FGravier GSébillot PBorghoff USchimmler S(2019)Using Knowledge Base Semantics in Context-Aware Entity LinkingProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345393(1-10)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3342558.3345393
Liu MGong GQin BLiu T(2019)A Multi-View–Based Collective Entity Linking MethodACM Transactions on Information Systems10.1145/330019737:2(1-29)Online publication date: 6-Feb-2019
https://dl.acm.org/doi/10.1145/3300197
Ponza MFerragina PChakrabarti S(2019)On computing entity relatedness in wikipedia, with applicationsKnowledge-Based Systems10.1016/j.knosys.2019.105051(105051)Online publication date: Sep-2019
https://doi.org/10.1016/j.knosys.2019.105051
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