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Manage. Inf. Syst."],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment\u2019s quality, this article proposes an Interactive Compact Genetic Algorithm\u00a0(ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm\u00a0(CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts\u2019 similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative\u2019s interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.<\/jats:p>","DOI":"10.1145\/3439772","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T02:08:36Z","timestamp":1621562916000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm"],"prefix":"10.1145","volume":"12","author":[{"given":"Xingsi","family":"Xue","sequence":"first","affiliation":[{"name":"Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, China and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, China and Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, China and School of Computer Science and Mathematics, Fujian University of Technology, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojing","family":"Wu","sequence":"additional","affiliation":[{"name":"Intelligent Information Processing Research Center, Fujian University of Technology China and School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Hohai University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.06.052"},{"key":"e_1_2_1_2_1","volume-title":"CEUR Workshop Proceedings","volume":"2536","author":"Algergawy Alsayed","year":"2019","unstructured":"Alsayed Algergawy, Daniel Faria, Alfio Ferrara, Irini Fundulaki, Ian Harrow, Sven Hertling, Ernesto Jimenez-Ruiz, et\u00a0al. 2019. 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