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A Bidirectional-Based Spreading Activation Method for Human Diseases Relatedness Detection Using Disease Ontology

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Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10448))

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Abstract

There is a numerous demand for a standard representation of the ubiquitous available information on the web. Developing an efficient algorithm for traversing large ontologies is a key challenge for many semantic web applications. This paper proposes spreading activation over ontology method based on bidirectional search technique in order to detect the relatedness between two human diseases. The aim of our work is to detect disease relatedness by considering semantic domain knowledge and description logic rules to identify diseases relatedness. The proposed method is divided into two phases: Semantic Matching and Disease Relatedness Detection. In Semantic matching phase, diseases in submitted query are semantically identified in the ontology graph. In Disease relatedness detection phase, disease relatedness is detected by running a bidirectional-based spreading activation algorithm and return the related path (set of diseases) if so. In addition, the classification of these diseases is provided as well.

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Notes

  1. 1.

    Thank you Dr. Diaa Elsayed, Gastroenterology and Hepatology specialist, for the Counseling.

  2. 2.

    http://www.disease-ontology.org.

  3. 3.

    http://www.violinet.org/vaccineontology.

  4. 4.

    http://infectiousdiseaseontology.org/page/Main_Page.

  5. 5.

    http://obi-ontology.org/page/Main_Page.

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Author information

Authors and Affiliations

  1. Enterprise Information Systems (EIS), University of Bonn, Bonn, Germany

    Said Fathalla

  2. Faculty of Science, Alexandria University, Alexandria, Egypt

    Said Fathalla

  3. Software Engineer, Alexandria, Egypt

    Yaman Kannot

Authors
  1. Said Fathalla
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  2. Yaman Kannot
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Corresponding author

Correspondence to Said Fathalla .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management , Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Fathalla, S., Kannot, Y. (2017). A Bidirectional-Based Spreading Activation Method for Human Diseases Relatedness Detection Using Disease Ontology. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-67074-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67073-7

  • Online ISBN: 978-3-319-67074-4

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