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Do Similar Entities Have Similar Embeddings?

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The Semantic Web (ESWC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14664))

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Abstract

Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph’s structure within their embedding space, i.e., position similar entities within the graph close to one another. This desirable property make KGEMs widely used in downstream tasks such as recommender systems or drug repurposing. Yet, the relation of entity similarity and similarity in the embedding space has rarely been formally evaluated. Typically, KGEMs are assessed based on their sole link prediction capabilities, using ranked-based metrics such as Hits@K or Mean Rank. This paper challenges the prevailing assumption that entity similarity in the graph is inherently mirrored in the embedding space. Therefore, we conduct extensive experiments to measure the capability of KGEMs to cluster similar entities together, and investigate the nature of the underlying factors. Moreover, we study if different KGEMs expose a different notion of similarity. Datasets, pre-trained embeddings and code are available at: https://github.com/nicolas-hbt/similar-embeddings/.

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Notes

  1. 1.

    We consider both ingoing and outgoing edges to define those neighborhoods.

  2. 2.

    Given, e.g., (SAP, headquarter, Germany), (BOSCH, headquarter, Germany), (Berlin, capitalOf, Germany), with common neighbors, SAP, BOSCH, and Berlin would be equally similar.

  3. 3.

    https://github.com/changyaochen/rbo/.

  4. 4.

    https://github.com/pykeen/pykeen/.

  5. 5.

    https://github.com/IBCNServices/pyRDF2Vec/.

  6. 6.

    https://github.com/bxshi/ConMask/.

  7. 7.

    https://www.microsoft.com/en-us/download/details.aspx?id=52312/.

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Author information

Authors and Affiliations

  1. Université de Lorraine, ERPI, Nancy, France

    Nicolas Hubert & Davy Monticolo

  2. Université de Lorraine, CNRS, LORIA, Nancy, France

    Nicolas Hubert & Armelle Brun

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

    Heiko Paulheim

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  1. Nicolas Hubert
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  2. Heiko Paulheim
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  3. Armelle Brun
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  4. Davy Monticolo
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Correspondence to Nicolas Hubert .

Editor information

Editors and Affiliations

  1. King’s College London, London, UK

    Albert Meroño Peñuela

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

    Anastasia Dimou

  3. EURECOM, Biot, France

    Raphaël Troncy

  4. Linköping University, Linköping, Sweden

    Olaf Hartig

  5. Technical University of Munich, Heilbronn, Germany

    Maribel Acosta

  6. Polytechnic Institute of Paris, Palaiseau, France

    Mehwish Alam

  7. University of Mannheim, Mannheim, Germany

    Heiko Paulheim

  8. EURECOM, Biot, France

    Pasquale Lisena

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Hubert, N., Paulheim, H., Brun, A., Monticolo, D. (2024). Do Similar Entities Have Similar Embeddings?. In: Meroño Peñuela, A., et al. The Semantic Web. ESWC 2024. Lecture Notes in Computer Science, vol 14664. Springer, Cham. https://doi.org/10.1007/978-3-031-60626-7_1

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