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International Semantic Web Conference

ISWC 2022: The Semantic Web – ISWC 2022 pp 575–591Cite as

Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13489)

Abstract

Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new Bio-ML track at OAEI 2022.

Resource type: Ontology Matching Dataset

License: CC BY 4.0 International

DOI: https://doi.org/10.5281/zenodo.6510086

Documentation: https://krr-oxford.github.io/DeepOnto/#/om_resources

OAEI track: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/

Keywords

  • Ontology Alignment
  • Equivalence matching
  • Subsumption matching
  • Evaluation resource
  • Biomedical ontology
  • OAEI

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  • DOI: 10.1007/978-3-031-19433-7_33
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Notes

  1. 1.

    https://mondo.monarchinitiative.org/.

  2. 2.

    http://oaei.ontologymatching.org/.

  3. 3.

    https://www.nlm.nih.gov/research/umls/index.html.

  4. 4.

    Mondo was working on official versioning, the information of current mappings is based on the preliminary release at: https://github.com/monarch-initiative/mondo/tree/master/src/ontology/mappings.

  5. 5.

    We exclude mappings involving missing class ids.

  6. 6.

    Compact IRI of a class in the form of ontology_prefix:class_ID.

  7. 7.

    The license to access UMLS is global and can be used to access SNOMED CT. We obtained SNOMED CT (and UMLS) after signing up to the UTS account and license following SNOMED and UMLS licensing in https://www.nlm.nih.gov/healthit/snomedct/snomed_licensing.html.

  8. 8.

    Labels are extracted from annotation properties concerning synonyms of the class name, e.g., rdfs:label, fma:synonym, skos:prefLabel, etc.

  9. 9.

    EditSim and BERTMap codes: https://github.com/KRR-Oxford/DeepOnto.

  10. 10.

    https://github.com/ernestojimenezruiz/logmap-matcher.

  11. 11.

    https://github.com/AgreementMakerLight/AML-Project.

  12. 12.

    BERTSubs codes: https://gitlab.com/chen00217/bert_subsumption; Word2Vec (or OWL2Vec*) + RF codes are in the folder Inter_Ontology/baselines/ of the this repository.

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Acknowledgments

This work was supported by the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project 237889), eBay, Samsung Research UK, Siemens AG, and the EPSRC projects OASIS (EP/S032347/1), UK FIRES (EP/S019111/1) and ConCur (EP/V050869/1). We would like to to thank the Mondo team, especially Nicolas Matentzoglu and Joe Flake, for their great help in creating the Mondo datasets.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, UK

    Yuan He, Jiaoyan Chen, Hang Dong & Ian Horrocks

  2. City, University of London, London, UK

    Ernesto Jiménez-Ruiz

  3. University of Oslo, Oslo, Norway

    Ernesto Jiménez-Ruiz

  4. Samsung Research, Staines, UK

    Ali Hadian

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  1. Yuan He
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  2. Jiaoyan Chen
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  4. Ernesto Jiménez-Ruiz
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Correspondence to Yuan He .

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Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Ulrike Sattler

  2. University of Chile, Santiago, Chile

    Aidan Hogan

  3. University of Cape Town, Cape Town, South Africa

    Maria Keet

  4. University of Bologna, Bologna, Italy

    Valentina Presutti

  5. Universidade Federal do Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  6. National Institute of Informatics, Tokyo, Japan

    Hideaki Takeda

  7. Orange, Belfort, France

    Pierre Monnin

  8. Sapienza University of Rome, Rome, Italy

    Giuseppe Pirrò

  9. University of Bari, Bari, Italy

    Claudia d’Amato

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He, Y., Chen, J., Dong, H., Jiménez-Ruiz, E., Hadian, A., Horrocks, I. (2022). Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. In: , et al. The Semantic Web – ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham. https://doi.org/10.1007/978-3-031-19433-7_33

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  • DOI: https://doi.org/10.1007/978-3-031-19433-7_33

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