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Detecting Human Diseases Relatedness: A Spreading Activation Approach Over Ontologies

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  • Said Fathalla

    (Bonn University, Bonn, Germany & Alexandria University, Alexandria, Egypt)

Abstract

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

Suggested Citation

  • Said Fathalla, 2018. "Detecting Human Diseases Relatedness: A Spreading Activation Approach Over Ontologies," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(3), pages 120-133, July.
    • Handle: RePEc:igg:jswis0:v:14:y:2018:i:3:p:120-133
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    Cited by:

    1. Nicola Capuano & Pasquale Foggia & Luca Greco & Pierluigi Ritrovato, 2022. "A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-22, January.

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