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Entity Linking with Wikidata: A Systematic Literature Review

delete2026-05-23
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PRE
AI
P
Philipp Scharpf *
T
Tinger, Corinna Brei
S
Spitz Andreas
N
Norman Meuschke
A
André Greiner-Petter
M
Moritz Schubotz
B
Bela Gipp
DOI:10.1145/3795134delete
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Abstract

Abstract

En 中文
This article provides a comprehensive systematic review of the literature on entity linking using Wikidata as the grounding knowledge base. Our review extends the scope of previous studies from two to eight dimensions of entity linking, which we classify into the following categories: definitions, tasks, types, domains, approaches, datasets, applications, and challenges. We find that datasets primarily address question-answering and news domains but underutilize Wikidata's capabilities for hyper-relations, multilingualism, and time dependence. The research gaps we identify include the need for more robust datasets, hybrid methods combining rule-based and learning-based approaches, and improved handling of ambiguity, sparse entity types, data noise, and knowledge graph evolution.
Keywords:
Entity linking
named entity recognition
wikidata

Journal

ACM Computing Surveys cover
ACM Computing Surveys
IF:
28
Papers:
2.4K
Citations:
3.5W

Organization

U
University of Gottingen
Scholars:
2.5W
Papers: 2.1W
Citations: 36
L
leibniz association
Scholars:
1.0K
Papers: 484
Citations: 0
U
university of konstanz
Scholars:
457
Papers: 256
Citations: 0
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