Using NSGA‐III for optimising biomedical ontology alignment. Issue 3 (3rd June 2019)

• Record Type: Journal Article
• Authors: Xue, Xingsi; Lu, Jiawei; Chen, Junfeng
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 3(2019)
• Page Start: 135
• Page End: 141
• Publication Date: 2019-06-03
• Subjects: genetic algorithms -- medical computing -- ontologies (artificial intelligence) -- medical information systems; similarity measures -- ontology partitioning technique -- large‐scale biomedical ontology matching problem -- ontology segment‐matching problems -- NSGA‐III -- anatomy track -- Ontology Alignment Evaluation Initiative -- biomedical ontology alignment -- biomedical information systems -- heterogeneous biomedical concepts -- nondominated sorting genetic algorithm‐III‐based biomedical ontology matching -- biomedical concept mapping; C1180 Optimisation techniques -- C6170K Knowledge engineering techniques -- C7140 Medical administration -- C7330 Biology and medical computing; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0014
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : To support semantic inter‐operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping's preference on the similarity measures significantly reduces the alignment's quality. In this study, a non‐dominated sorting genetic algorithm (NSGA)‐III‐based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large‐scale biomedical ontology matching problem into several ontology segment‐matching problems, and then uses NSGA‐III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of the authors' approach.