{"title":"Automatic Knowledge Discovery and Semantic Annotation for Web Services","authors":"Szu-Yin Lin, Chia-Chen Chung, Wei-Che Hu, Chihli Hung","doi":"10.1109/ICEBE.2015.69","DOIUrl":null,"url":null,"abstract":"OWL-S (formally DAML-S) is an OWL-based Web services ontology, which gives the ability for describing the semantics of Web services and their capabilities in a formal and machine-process able manner. Moreover, it is helpful to semantic services matching, selection, and composition. However, it is a very complicated and heavy task to annotate semantic Web services manually by human. In this paper, we proposed a methodology to discover knowledge from the history data and profiles of Web services, and then semantic annotate them automatically. With the proposed approach, semantic relations between Web services could be extracted by combining association rules and input/output matching. We also give a scenario to explain how the proposed methodology works.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 12th International Conference on e-Business Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEBE.2015.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
OWL-S (formally DAML-S) is an OWL-based Web services ontology, which gives the ability for describing the semantics of Web services and their capabilities in a formal and machine-process able manner. Moreover, it is helpful to semantic services matching, selection, and composition. However, it is a very complicated and heavy task to annotate semantic Web services manually by human. In this paper, we proposed a methodology to discover knowledge from the history data and profiles of Web services, and then semantic annotate them automatically. With the proposed approach, semantic relations between Web services could be extracted by combining association rules and input/output matching. We also give a scenario to explain how the proposed methodology works.
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