A method for exploring implicit concept relatedness in biomedical knowledge network

BMC Bioinformatics

Research

Ye Wang¹  Lan Huang²  Tian Bai²  Yan Wang²  Leiguang Gong³  Casimir A. Kulikowski⁴ 

[1] College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China;College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 2699 Qianjin St, Changchun, China;College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China;Yantai Intelligent Information Technologies Ltd., 2699 Qianjin St, Yantai, China;Department of Computer Science, Rutgers, The State University of New Jersey, 2699 Qianjin St, Piscataway, NJ, USA;

关键词: Biomedical ontology;    Knowledge network;    Implicit relatedness;   

DOI  :  10.1186/s12859-016-1131-5

来源: Springer

【 摘 要 】

BackgroundBiomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community.ResultsIn this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network.ConclusionsExperiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery.

【 授权许可】

CC BY   
© The Author(s) 2016

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