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Structural Bias in Knowledge Graphs for the Entity Alignment Task

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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

Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain and play a central role in a multitude of AI tasks like recommendations and query answering. Recent works have revealed that KG embedding methods used to implement these tasks often exhibit direct forms of bias (e.g., related to gender, nationality, etc.) leading to discrimination. In this work, we are interested in the impact of indirect forms of bias related to the structural diversity of KGs in entity alignment (EA) tasks. In this respect, we propose an exploration-based sampling algorithm, SUSIE, that generates challenging benchmark data for EA methods, with respect to structural diversity. SUSIE requires setting the value of a single hyperparameter, which affects the connectivity of the generated KGs. The generated samples exhibit similar characteristics to some of the most challenging real-world KGs for EA tasks. Using our sampling, we demonstrate that state-of-the-art EA methods, like RREA, RDGCN, MultiKE and PARIS, exhibit different robustness to structurally diverse input KGs.

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Notes

  1. 1.

    https://github.com/fanourakis/Sampling_for_Entity_Alignment.git.

  2. 2.

    As opposed to strongly connected components, where edge directions matter.

  3. 3.

    According to the sampling taxonomy proposed in [34].

  4. 4.

    https://github.com/MaoXinn/RREA.

  5. 5.

    https://github.com/nju-websoft/OpenEA.

  6. 6.

    https://github.com/epfl-dlab/entity-matchers.

  7. 7.

    https://www.csd.uoc.gr/~vefthym/minoanER/datasets.html.

  8. 8.

     [18, 44] investigate direct forms of bias on “flat”, tabular data.

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Acknowledgement

The work of N. Fanourakis and V. Efthymiou was funded from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under GA No 969.

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Authors and Affiliations

  1. FORTH-ICS, Heraklion, Greece

    Nikolaos Fanourakis & Vasilis Efthymiou

  2. Lab. ETIS, CY Cergy Paris University, ENSEA, CNRS UMR 8051, Cergy, France

    Vassilis Christophides & Dimitris Kotzinos

  3. University of Ioannina, Ioannina, Greece

    Evaggelia Pitoura

  4. Tampere University, Tampere, Finland

    Kostas Stefanidis

Authors
  1. Nikolaos Fanourakis
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  2. Vasilis Efthymiou
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  3. Vassilis Christophides
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  4. Dimitris Kotzinos
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  5. Evaggelia Pitoura
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  6. Kostas Stefanidis
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Corresponding author

Correspondence to Nikolaos Fanourakis .

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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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Fanourakis, N., Efthymiou, V., Christophides, V., Kotzinos, D., Pitoura, E., Stefanidis, K. (2023). Structural Bias in Knowledge Graphs for the Entity Alignment Task. 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_5

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_5

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