Jingtao Zhou, Shusheng Zhang, Mingwei Wang, Han Zhao, Chao Zhang, Peng Li, Xiaofeng Dong, Kefei Wang
{"title":"Element matching by concatenating linguistic-based matchers and constraint-based matcher","authors":"Jingtao Zhou, Shusheng Zhang, Mingwei Wang, Han Zhao, Chao Zhang, Peng Li, Xiaofeng Dong, Kefei Wang","doi":"10.1109/ICTAI.2005.64","DOIUrl":null,"url":null,"abstract":"Although a lot of previous work on schema matching has developed many partial automatic matches for specific application domains, combining multiple match techniques enables achieving high accuracy for a large variety of match circumstances. In this context, we present a schema-based element matching approach that concatenates linguistic-based matchers and a constraint-based matcher. We propose a basic processing of our element level match approach in terms of a sequence of linguistic-based match and constraint-based match. We also provide a composite element name matcher to automatically combine linguistic-based match algorithms with a maximum priority strategy, and a neural network matcher to categorize elements of schemas by using element constraints with results from composite name matcher for joint consideration of multiple criteria. The concatenation of composite name matcher and neural network matcher enable our approach to adapt to more complex matching circumstance","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"30 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2005.64","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Although a lot of previous work on schema matching has developed many partial automatic matches for specific application domains, combining multiple match techniques enables achieving high accuracy for a large variety of match circumstances. In this context, we present a schema-based element matching approach that concatenates linguistic-based matchers and a constraint-based matcher. We propose a basic processing of our element level match approach in terms of a sequence of linguistic-based match and constraint-based match. We also provide a composite element name matcher to automatically combine linguistic-based match algorithms with a maximum priority strategy, and a neural network matcher to categorize elements of schemas by using element constraints with results from composite name matcher for joint consideration of multiple criteria. The concatenation of composite name matcher and neural network matcher enable our approach to adapt to more complex matching circumstance