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Survey on English Entity Linking on Wikidata

Paper · 2021 · Cedric Möller, Jens Lehmann and Ricardo Usbeck

Summary

Entity Linking (EL) connects mentions in text to entries in a knowledge graph. Wikidata is an appealing target for EL. A large community edits it and it changes often. Its facts sit under language-independent identifiers, and its statements can carry qualifiers, which makes the graph hyper-relational. The authors ask whether current EL datasets and systems actually take advantage of these traits. They searched five academic search engines through December 2020 and cut roughly 150 hits down to 15 papers plus one master thesis. They also collected 11 datasets that carry Wikidata IDs from the outset. Each system and dataset is checked for which Wikidata features it relies on. The answer is mostly no. The datasets are annotated the same way as DBpedia-oriented ones, so multilingual and time-aware benchmarks are absent. The systems mostly treat Wikidata as a generic graph: they use labels and sometimes descriptions, but ignore qualifiers and often types. Many add Wikipedia text to make up for Wikidata's thin prose. The survey maps a young field and points to concrete open directions.

Key points

  • Selection pipeline: about 150 candidate papers were filtered to 15 papers and 1 thesis, all published 2018–2020. English support was required, and semi-structured EL (e.g., tables) was excluded.
  • 11 datasets met the criteria: T-REx, NYT2018, ISTEX-1000, LC-QuAD 2.0, KnowledgeNet, CLEF HIPE 2020, KORE50DYWC, Mewsli-9, the Kensho Derived Wikimedia Dataset, TweekiData and TweekiGold.
  • A dataset counted only if it held Wikidata identifiers from the start. Corpora remapped to Wikidata later were excluded.
  • Reported scores:
    • On AIDA-CoNLL, a language-model-based linker reaches about 0.95.
    • On Mewsli-9, the multilingual model of Botha et al. reports about 0.91 recall.
  • KG-agnostic linkers (AGDISTIS, MAG, DoSeR) use labels, graph structure and types, but none of them uses hyper-relational qualifiers. Suggested remedies include hyper-relational graph embeddings and richer type information.

Interesting ideas

  • I1. In MAG, candidate lookup runs as a cascade: acronym check, then normalization (casing, special characters, camel-case splitting), then exact match, then trigram similarity, and finally stemming as a last resort. This gives cheap, explainable recall before any heavy scoring.
  • I2. For collective disambiguation, MAG builds a K-partite graph whose edges join only candidates of different mentions, then runs PageRank on it; using a complete graph instead made results worse.
  • I3. In MAG, the random-jump probability in PageRank depends on each entity's prior popularity, so the frequency prior is folded into graph coherence in a principled way.
  • I4. DoSeR splits knowledge sources into core graphs, which supply the link targets, and optional graphs, which only add evidence.
  • I5. The authors argue for time-dependent benchmarks and for evaluating against fixed full dumps, because a frequently changing graph makes results hard to reproduce.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): Mova could resolve noun phrases against its WordNet/Wikidata ontology with the same ordered fallbacks, logging which step fired so every link can be justified.
  • I2 (world): when several entities in one fable or text must be grounded together, coherence scoring could link only cross-mention candidates, keeping the reasoning traceable and avoiding self-reinforcement among alternatives.
  • I3 (world): a knowledge-graph ranking step could blend corpus frequency into graph walks, turning the "usually means X" default into an explicit, inspectable parameter.
  • I4 (world): Mova's compiled knowledge could separate the ontology it links into from auxiliary resources like FrameNet or Wikipedia text that only support decisions.
  • I5 (learn): evaluations that depend on Wikidata should pin a specific dump version and add held-out cases where the correct entity changed over time, as a kind of negative control.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Universität HamburgHamburg · Germany39 works · 4,669 citations🎓University of BonnBonn · Germany5 works · 5,729 citations🔬Fraunhofer Institute for Intelligent Analysis and Information SystemsSankt Augustin · Germany5 works · 3,853 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 3Research subjects and languages the work is about.English · 1,373 worksNamed entity recognition · 101 worksKnowledge graphs & ontologies · 103 works

Cite

Cite as Cedric Möller, Jens Lehmann and Ricardo Usbeck. 2021. Survey on English Entity Linking on Wikidata. arXiv:2112.01989.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2112.01989 · arXiv 2112.01989

@misc{moller2021survey,
  title = {{Survey on English Entity Linking on Wikidata}},
  author = {Cedric Möller, Jens Lehmann and Ricardo Usbeck},
  year = {2021},
  doi = {10.48550/arxiv.2112.01989},
  eprint = {2112.01989},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2112.01989}
}

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