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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:p> Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment\u2019s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm\u2019s performance and the alignment\u2019s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert\u2019s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)\u2019s benchmark to test our proposal\u2019s performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment\u2019s quality, and it also outperforms OAEI\u2019s participants. <\/jats:p>","DOI":"10.1142\/s0218001422570026","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T08:44:59Z","timestamp":1650530699000},"source":"Crossref","is-referenced-by-count":8,"title":["Semi-Automatic Ontology Matching Based on Interactive Compact Genetic Algorithm"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3008-8782","authenticated-orcid":false,"given":"Xingsi","family":"Xue","sequence":"first","affiliation":[{"name":"Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian 350118, P. R. 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