Entity Linking with Wikidata: A Systematic Literature Review. Scharpf, P., Breitinger, C., Spitz, A., Meuschke, N., Greiner-Petter, A., Schubotz, M., & Gipp, B. ACM Computing Surveys, 58(9):227:1–227:50, February, 2026. Journal Rank Q1; IF: 26.3![Entity Linking with Wikidata: A Systematic Literature Review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download 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.
@article{BibbaseScharpfBSM26,
title = {Entity {Linking} with {Wikidata}: {A} {Systematic} {Literature} {Review}},
volume = {58},
issn = {0360-0300},
shorttitle = {Entity {Linking} with {Wikidata}},
url = {https://dl.acm.org/doi/epdf/10.1145/3795134},
doi = {10.1145/3795134},
abstract = {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.},
number = {9},
journal = {ACM Computing Surveys},
author = {Scharpf, Philipp and Breitinger, Corinna and Spitz, Andreas and Meuschke, Norman and Greiner-Petter, André and Schubotz, Moritz and Gipp, Bela},
month = feb,
year = {2026},
note = {Journal Rank Q1; IF: 26.3},
pages = {227:1--227:50},
}
Downloads: 1
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