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PreMedOnto: A Computer Assisted Ontology for Precision Medicine

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Natural Language Processing and Information Systems (NLDB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11608))

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Abstract

This paper proposes an ontology learning framework that combines text mining, information extraction and retrieval. The proposed model takes advantage of existing structured knowledge by reusing terms and concepts from other ontologies. We further apply the methodology to create a detailed ontology for the emerging precision medicine (PM) domain by collecting a corpus of relevant articles and mapping its frequent terms to existing concepts. The resulting ontology consists of 543 annotated classes. The ontology was also tested for effectiveness by applying two evaluation frameworks to validate its design and quality. The results demonstrate that the ontology learning system is able to capture and represent the semantics of the PM domain with high precision and significance. Moreover, the computer-assisted construction process reduced dependency on expert knowledge. The developed PreMedOnto ontology could be further used to enhance the potentials of other NLP applications in the PM domain.

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Notes

  1. 1.

    https://metamap.nlm.nih.gov/.

  2. 2.

    https://bioportal.bioontology.org/ontologies/NCIT.

  3. 3.

    https://bioportal.bioontology.org/ontologies/MESH.

  4. 4.

    https://bioportal.bioontology.org/ontologies/IOBC.

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Authors and Affiliations

  1. Computer Engineering Department, College of Engineering, Arab Academy for Science, Technology, and Maritime Transport (AAST), Alexandria, 1029, Egypt

    Noha S. Tawfik

  2. Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

    Noha S. Tawfik & Marco R. Spruit

Authors
  1. Noha S. Tawfik
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  2. Marco R. Spruit
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Corresponding author

Correspondence to Noha S. Tawfik .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, UK

    Farid Meziane

  3. University of Salford, Salford, UK

    Sunil Vadera

  4. Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  5. CSE, University of Salford, Salford, UK

    Mohamad Saraee

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Tawfik, N.S., Spruit, M.R. (2019). PreMedOnto: A Computer Assisted Ontology for Precision Medicine . In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_28

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  • DOI: https://doi.org/10.1007/978-3-030-23281-8_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23280-1

  • Online ISBN: 978-3-030-23281-8

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