{"title":"面向重用的自动本体识别","authors":"M. Speretta, S. Gauch","doi":"10.1109/WI.2007.24","DOIUrl":null,"url":null,"abstract":"The increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Automatic Ontology Identification for Reuse\",\"authors\":\"M. Speretta, S. Gauch\",\"doi\":\"10.1109/WI.2007.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.\",\"PeriodicalId\":192501,\"journal\":{\"name\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2007.24\",\"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/WIC/ACM International Conference on Web Intelligence (WI'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2007.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.