Using NSGA-III for optimising biomedical ontology alignment

• Published in: CAAI Transactions on Intelligence Technology Vol. 4; no. 3; pp. 135 - 141
• Main Authors: Xue, Xingsi, Lu, Jiawei, Chen, Junfeng
• Format: Journal Article
• Language: English
• Published: Beijing The Institution of Engineering and Technology 01.09.2019; John Wiley & Sons, Inc; Wiley
• Subjects: Alignment; anatomy track; biomedical concept mapping; biomedical information systems; biomedical ontology alignment; C1180 Optimisation techniques; C6170K Knowledge engineering techniques; C7140 Medical administration; C7330 Biology and medical computing; Coma; Concept mapping; Genetic algorithms; heterogeneous biomedical concepts; Information systems; large-scale biomedical ontology matching problem; Linguistics; Matching; medical computing; medical information systems; nondominated sorting genetic algorithm-III-based biomedical ontology matching; NSGA-III; ontologies (artificial intelligence); Ontology; Ontology Alignment Evaluation Initiative; ontology partitioning technique; ontology segment-matching problems; Optimization; Preferences; Similarity; Similarity measures; Sorting algorithms; Special Issue: Advances in Bio-inspired Heuristics for Computing; Taxonomy; large-scale biomedical ontology matching problem; NSGA-III; medical information systems; genetic algorithms; biomedical concept mapping; similarity measures; biomedical ontology alignment; biomedical information systems; ontology partitioning technique; ontologies (artificial intelligence); Ontology Alignment Evaluation Initiative; anatomy track; medical computing; nondominated sorting genetic algorithm-III-based biomedical ontology matching; ontology segment-matching problems; heterogeneous biomedical concepts

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 make...