{"title":"基于知识的系统的概念图式","authors":"J. Sowa","doi":"10.1145/800227.806920","DOIUrl":null,"url":null,"abstract":"Knowledge-based systems are data bases with more powerful front ends for dealing with the meaning of data. This paper discusses requirements for a conceptual schema that is general enough to support knowledge bases as well as ordinary data bases. It presents seven features that such a schema must support and evaluates various approaches to data base semantics in terms of them. The AI notations for semantic networks or conceptual graphs are highly general ones that can support all seven features.","PeriodicalId":347815,"journal":{"name":"Workshop on Data Abstraction, Databases and Conceptual Modelling","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1981-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"A conceptual schema for Knowledge-based systems\",\"authors\":\"J. Sowa\",\"doi\":\"10.1145/800227.806920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Knowledge-based systems are data bases with more powerful front ends for dealing with the meaning of data. This paper discusses requirements for a conceptual schema that is general enough to support knowledge bases as well as ordinary data bases. It presents seven features that such a schema must support and evaluates various approaches to data base semantics in terms of them. The AI notations for semantic networks or conceptual graphs are highly general ones that can support all seven features.\",\"PeriodicalId\":347815,\"journal\":{\"name\":\"Workshop on Data Abstraction, Databases and Conceptual Modelling\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1981-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Data Abstraction, Databases and Conceptual Modelling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/800227.806920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Data Abstraction, Databases and Conceptual Modelling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/800227.806920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Knowledge-based systems are data bases with more powerful front ends for dealing with the meaning of data. This paper discusses requirements for a conceptual schema that is general enough to support knowledge bases as well as ordinary data bases. It presents seven features that such a schema must support and evaluates various approaches to data base semantics in terms of them. The AI notations for semantic networks or conceptual graphs are highly general ones that can support all seven features.
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