TY - GEN
T1 - Implicit knowledge discovery in biomedical ontologies
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
AU - Bai, Tian
AU - Gong, Leiguang
AU - Kulikowski, Casimir A.
AU - Huang, Lan
N1 - Funding Information:
This work is supported in part by China Postdoctoral Science Foundation (2014M561293), the National Natural Science Foundation of China (No.61300147; 61472159; 61572227), Science and Technology Planning Project of Jilin Province, China (2014NI43; 20150520064JH; 20130lO1179JC-03), the Science and Technology Program of Changchun (No.14GHOI4), and Interdisciplinary Studies Foundation from Jilin University (JCKY-QKJC41).
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery-a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.
AB - Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery-a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.
UR - http://www.scopus.com/inward/record.url?scp=84962356017&partnerID=8YFLogxK
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U2 - 10.1109/BIBM.2015.7359734
DO - 10.1109/BIBM.2015.7359734
M3 - Conference contribution
AN - SCOPUS:84962356017
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 497
EP - 502
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 November 2015 through 12 November 2015
ER -