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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Said Fathalla, Yaman Kannot

Erschienen in: Computational Collective Intelligence

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Mapping the Territory for a Knowledge-Based System

Nächstes Kapitel Semantic Networks Modeling with Operand-Operator Structures in Association-Oriented Metamodel

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

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

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

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

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

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Titel: A Bidirectional-Based Spreading Activation Method for Human Diseases Relatedness Detection Using Disease Ontology
verfasst von: Said Fathalla
Yaman Kannot
Verlag: Springer International Publishing
Buch: Computational Collective Intelligence
Print ISBN: 978-3-319-67073-7

Electronic ISBN: 978-3-319-67074-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67074-4_2

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