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Entity Linking for KGQA Using AMR Graphs

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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 is an essential part of analytical systems for question answering on knowledge graphs (KGQA). The mentioned entity has to be spotted in the text and linked to the correct resource in the knowledge graph (KG). With this paper, we present our approach on entity linking using the abstract meaning representation (AMR) of the question to spot the surface forms of entities. We re-trained AMR models with automatically generated training data. Based on these models, we extract surface forms and map them to an entity dictionary of the desired KG. For the disambiguation process, we evaluated different options and configurations on QALD-9 and LC-QuaD 2.0. The results of the best performing configurations outperform existing entity linking approaches.

N. Steinmetz—Work was done while at TU Ilmenau.

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Notes

  1. 1.

    Unfortunately, the Lexicalization dataset is not available anymore.

  2. 2.

    http://nlp.stanford.edu/software/CRF-NER.shtml.

  3. 3.

    https://github.com/bjascob/amrlib.

  4. 4.

    https://github.com/jflanigan/jamr.

  5. 5.

    The newest model is trained on 2016 LDC training data.

  6. 6.

    as of December 2022.

  7. 7.

    https://catalog.ldc.upenn.edu/LDC2020T02.

  8. 8.

    c.f. https://amr.isi.edu/index.html.

  9. 9.

    http://www.isi.edu/~ulf/amr/help/amr-guidelines.pdf.

  10. 10.

    as shown in [9, 17].

  11. 11.

    https://catalog.ldc.upenn.edu/LDC2020T02.

  12. 12.

    cf. the training dataset of the SMART task challenge 2022: https://smart-task.github.io/2022/.

  13. 13.

    https://huggingface.co/docs/transformers/model_doc/pegasus.

  14. 14.

    https://amr.isi.edu/smatch-13.pdf.

  15. 15.

    The SMATCH score for BART Large is stated as 0.837 trained on LDC AMR 3.0.

  16. 16.

    https://doi.org/10.5281/zenodo.7442882.

  17. 17.

    instructions on how to use the models with amrlib can be found here: https://github.com/bjascob/amrlib-models.

  18. 18.

    which would not be helpful for that disambiguation case.

  19. 19.

    https://zenodo.org/record/6607514.

  20. 20.

    Falcon: https://labs.tib.eu/falcon/falcon2/api-use,

    Spotlight: https://www.dbpedia-spotlight.org/api,

    TagMe: https://sobigdata.d4science.org/web/tagme/tagme-help.

  21. 21.

    The authors do not wish to use the JSON web service for evaluation comparison and it also responses with HTTP 404 as of December 10th 2022.

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Acknowledgement

The author wants to thank Khaoula Benmaarouf and Kanchan Shivashankar for their help in preparing some of the data utilized for this paper.

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Authors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Germany

    Nadine Steinmetz

  2. Bergische Universität Wuppertal, Wuppertal, Germany

    Nadine Steinmetz

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  1. Nadine Steinmetz
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Correspondence to Nadine Steinmetz .

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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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Steinmetz, N. (2023). Entity Linking for KGQA Using AMR Graphs. 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_8

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_8

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