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EBOCA: Evidences for BiOmedical Concepts Association Ontology

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Knowledge Engineering and Knowledge Management (EKAW 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13514))

Abstract

There is a large number of online documents data sources available nowadays. The lack of structure and the differences between formats are the main difficulties to automatically extract information from them, which also has a negative impact on its use and reuse. In the biomedical domain, the DISNET platform emerged to provide researchers with a resource to obtain information in the scope of human disease networks by means of large-scale heterogeneous sources. Specifically in this domain, it is critical to offer not only the information extracted from different sources, but also the evidence that supports it. This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations; with the objective of providing an schema to improve the publication and description of evidences and biomedical associations in this domain. The ontology has been successfully evaluated to ensure there are no errors, modelling pitfalls and that it meets the previously defined functional requirements. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed according to the proposed ontology to create a Knowledge Graph that can be used in real scenarios, and which has also been used for the evaluation of the presented ontology.

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Notes

  1. 1.

    https://disnet.ctb.upm.es/.

  2. 2.

    https://bioportal.bioontology.org/.

  3. 3.

    https://ncithesaurus.nci.nih.gov/.

  4. 4.

    https://uts.nlm.nih.gov/uts/.

  5. 5.

    https://bio2rdf.org/.

  6. 6.

    https://disease-ontology.org/.

  7. 7.

    https://www.orpha.net/.

  8. 8.

    https://nsides.io/.

  9. 9.

    https://www.dublincore.org/specifications/dublin-core/dcmi-terms/.

  10. 10.

    https://w3id.org/eboca/portal.

  11. 11.

    https://w3id.org/eboca/sem-disnet, https://w3id.org/eboca/evidences.

  12. 12.

    https://w3id.org/eboca/portal.

  13. 13.

    https://w3id.org/eboca/sem-disnet.

  14. 14.

    https://medal.ctb.upm.es/internal/gitlab/disnet/sem-disnet/blob/master/diagrams/SEM-DISNET_hierarchy.png.

  15. 15.

    https://w3id.org/eboca/evidences.

  16. 16.

    https://www.dublincore.org/specifications/dublin-core/dcmi-terms/.

  17. 17.

    https://drugs4covid.github.io/EBOCA-portal/requirements/requirements-evidences.html.

  18. 18.

    https://github.com/drugs4covid/EBOCA-Resources.

  19. 19.

    https://blazegraph.com/.

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Acknowledgments

This work is supported by the DRUGS4COVID++ project, funded by Ayudas Fundación BBVA a equipos de investigación científica SARS-CoV-2 y COVID-19. The work is also supported by “Data-driven drug repositioning applying graph neural networks (3DR-GNN)” under grant “PID2021-122659OB-I00” from the Spanish Ministerio de Ciencia, Innovación y Universidades. LPS’s work is supported by “Programa de fomento de la investigación y la innovación (Doctorados Industriales)” from Comunidad de Madrid (grant “IND2019/TIC-17159”).

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Authors and Affiliations

  1. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain

    Andrea Álvarez Pérez, Lucía Prieto Santamaría & Alejandro Rodríguez-González

  2. Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain

    Ana Iglesias-Molina, María Poveda-Villalón & Carlos Badenes-Olmedo

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  1. Andrea Álvarez Pérez
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  2. Ana Iglesias-Molina
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  3. Lucía Prieto Santamaría
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  4. María Poveda-Villalón
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  5. Carlos Badenes-Olmedo
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  6. Alejandro Rodríguez-González
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Correspondence to Alejandro Rodríguez-González .

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Editors and Affiliations

  1. Univ. Politecnica de Madrid, Boadilla del Monte, Spain

    Oscar Corcho

  2. CWI, Amsterdam, The Netherlands

    Laura Hollink

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Oliver Kutz

  4. Free University of Bozen-Bolzano, Bolzano, Italy

    Nicolas Troquard

  5. TU Wien, Vienna, Austria

    Fajar J. Ekaputra

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Pérez, A.Á., Iglesias-Molina, A., Santamaría, L.P., Poveda-Villalón, M., Badenes-Olmedo, C., Rodríguez-González, A. (2022). EBOCA: Evidences for BiOmedical Concepts Association Ontology. In: Corcho, O., Hollink, L., Kutz, O., Troquard, N., Ekaputra, F.J. (eds) Knowledge Engineering and Knowledge Management. EKAW 2022. Lecture Notes in Computer Science(), vol 13514. Springer, Cham. https://doi.org/10.1007/978-3-031-17105-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-17105-5_11

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