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NASTyLinker: NIL-Aware Scalable Transformer-Based Entity Linker

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The Semantic Web (ESWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13870))

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Abstract

Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.

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Notes

  1. 1.

    https://github.com/nheist/CaLiGraph.

  2. 2.

    While there are entities in Wikidata which do not have a Wikipedia page, this case does not occur in NILK by construction.

  3. 3.

    We implement further clustering metrics (B-Cubed+, CEAF, MUC) but do not list them as they are similar to or adaptations of the classification metrics.

  4. 4.

    We apply simple preprocessing like lower-casing and removal of special characters.

  5. 5.

    We tried to compare with the full approach of Agarwal et al. but they do not provide any code and our efforts to re-implement it did not yield improved results.

  6. 6.

    The sampling of clusters was stratified w.r.t. cluster size.

  7. 7.

    We evaluated the linking and clustering decision w.r.t. the top-4 mention and entity candidates produced by the bi-encoder. Although recall@4 for the bi-encoder is 97%, some relevant candidates might have been missed.

  8. 8.

    For the evaluation to be significant, we treat all clusters referring to the same known entity as a single cluster.

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Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Nicolas Heist & Heiko Paulheim

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  1. Nicolas Heist
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  2. Heiko Paulheim
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Correspondence to Nicolas Heist .

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Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  2. University of London, London, UK

    Ernesto Jimenez-Ruiz

  3. Rensselaer Polytechnic Institute, Troy, MI, USA

    Jamie McCusker

  4. Universidade de Lisboa, Lisbon, Portugal

    Daniel Faria

  5. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  6. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  7. EURECOM, Biot, France

    Raphael Troncy

  8. University of Mannheim, Mannheim, Germany

    Sven Hertling

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Heist, N., Paulheim, H. (2023). NASTyLinker: NIL-Aware Scalable Transformer-Based Entity Linker. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_11

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