{"title":"从关系模式中自动学习本体","authors":"Lu Yiqing, Liu Lu, Li Chen","doi":"10.1109/ISRA.2012.6219258","DOIUrl":null,"url":null,"abstract":"In order to knowledge understanding and communication between different areas, knowledge sharing and coordination require a general conception framework of enterprise knowledge expression and communication, and form shared concept protocol in supply chain. There're numerous useful history data in existing database of enterprise, and actually relational database contains concept model of related areas. This paper proposes 11 mapping rules to convert relational database schema to ontology. Several actual cases are present to explain these mapping rules, and comparison to some other existing methods is also discussed at the end of this paper.","PeriodicalId":266930,"journal":{"name":"2012 IEEE Symposium on Robotics and Applications (ISRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Automatic learning ontology from relational schema\",\"authors\":\"Lu Yiqing, Liu Lu, Li Chen\",\"doi\":\"10.1109/ISRA.2012.6219258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to knowledge understanding and communication between different areas, knowledge sharing and coordination require a general conception framework of enterprise knowledge expression and communication, and form shared concept protocol in supply chain. There're numerous useful history data in existing database of enterprise, and actually relational database contains concept model of related areas. This paper proposes 11 mapping rules to convert relational database schema to ontology. Several actual cases are present to explain these mapping rules, and comparison to some other existing methods is also discussed at the end of this paper.\",\"PeriodicalId\":266930,\"journal\":{\"name\":\"2012 IEEE Symposium on Robotics and Applications (ISRA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Symposium on Robotics and Applications (ISRA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISRA.2012.6219258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Symposium on Robotics and Applications (ISRA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRA.2012.6219258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic learning ontology from relational schema
In order to knowledge understanding and communication between different areas, knowledge sharing and coordination require a general conception framework of enterprise knowledge expression and communication, and form shared concept protocol in supply chain. There're numerous useful history data in existing database of enterprise, and actually relational database contains concept model of related areas. This paper proposes 11 mapping rules to convert relational database schema to ontology. Several actual cases are present to explain these mapping rules, and comparison to some other existing methods is also discussed at the end of this paper.