{"title":"基于领域本体的Web服务搜索","authors":"Xu Bin, Wang Yan, Zhang Po, Li Juanzi","doi":"10.1109/SOSE.2005.42","DOIUrl":null,"url":null,"abstract":"Searching proper Web services is the basic step to composite Web services into applications. Current searching in UDDI servers is based on taxonomy and tModel, which is not convenient to find domain related Web services. In this paper, we propose a method to search Web services based on domain ontology. Firstly the WSDL crawler collects the WSDL files from the Internet resources like Google, Baidu and XMethods as much as possible. Secondly ontology is used to represent the domain, such as travel ontology. Thirdly a support vector machine (SVM) classifier is constructed to select the domain WSDL files from the collected WSDL files; domain vector is built according to the domain ontology, and features are extracted from WSDL files to train the SVM classifier. Finally we evaluate the method through experiment and show that the method is effective.","PeriodicalId":229065,"journal":{"name":"IEEE International Workshop on Service-Oriented System Engineering (SOSE'05)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Web services searching based on domain ontology\",\"authors\":\"Xu Bin, Wang Yan, Zhang Po, Li Juanzi\",\"doi\":\"10.1109/SOSE.2005.42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Searching proper Web services is the basic step to composite Web services into applications. Current searching in UDDI servers is based on taxonomy and tModel, which is not convenient to find domain related Web services. In this paper, we propose a method to search Web services based on domain ontology. Firstly the WSDL crawler collects the WSDL files from the Internet resources like Google, Baidu and XMethods as much as possible. Secondly ontology is used to represent the domain, such as travel ontology. Thirdly a support vector machine (SVM) classifier is constructed to select the domain WSDL files from the collected WSDL files; domain vector is built according to the domain ontology, and features are extracted from WSDL files to train the SVM classifier. Finally we evaluate the method through experiment and show that the method is effective.\",\"PeriodicalId\":229065,\"journal\":{\"name\":\"IEEE International Workshop on Service-Oriented System Engineering (SOSE'05)\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Workshop on Service-Oriented System Engineering (SOSE'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SOSE.2005.42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Workshop on Service-Oriented System Engineering (SOSE'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOSE.2005.42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Searching proper Web services is the basic step to composite Web services into applications. Current searching in UDDI servers is based on taxonomy and tModel, which is not convenient to find domain related Web services. In this paper, we propose a method to search Web services based on domain ontology. Firstly the WSDL crawler collects the WSDL files from the Internet resources like Google, Baidu and XMethods as much as possible. Secondly ontology is used to represent the domain, such as travel ontology. Thirdly a support vector machine (SVM) classifier is constructed to select the domain WSDL files from the collected WSDL files; domain vector is built according to the domain ontology, and features are extracted from WSDL files to train the SVM classifier. Finally we evaluate the method through experiment and show that the method is effective.