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Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result\u2019s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment\u2019s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies\u2019 heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)\u2019s testing cases and two pairs of real sensor ontologies.<\/jats:p>","DOI":"10.1155\/2020\/6666228","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T02:05:07Z","timestamp":1606356307000},"page":"1-10","source":"Crossref","is-referenced-by-count":2,"title":["Sensor Ontology Metamatching with Heterogeneity Measures"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3008-8782","authenticated-orcid":true,"given":"Xingsi","family":"Xue","sequence":"first","affiliation":[{"name":"Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian 350118, China"},{"name":"Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian 350118, China"},{"name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China"},{"name":"Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian 350118, China"},{"name":"Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, Fujian 350118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5408-8309","authenticated-orcid":true,"given":"Jiawei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian 350118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3220-7287","authenticated-orcid":true,"given":"Chengcai","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1128-2141","authenticated-orcid":true,"given":"Yikun","family":"Huang","sequence":"additional","affiliation":[{"name":"Concord University College, Fujian Normal University, Fuzhou 350117, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3390\/s20072056"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.09.017"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.03.007"},{"volume-title":"Evolutionary Algorithm based Ontology Matching Technique","year":"2018","author":"X. 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