{"title":"使用推断的语义链接搜索文件系统","authors":"Deepavali Bhagwat, N. Polyzotis","doi":"10.1145/1083356.1083372","DOIUrl":null,"url":null,"abstract":"We describe Eureka, a file system search engine that takes into account the inherent relationships among files in order to improve the rankings of search results. The key idea is to automatically infer semantic links within the file system, and use the structure of the links to determine the importance of different files and essentially bias the result rankings. We discuss the inference of semantic links and describe the design of the Eureka search engine.","PeriodicalId":134809,"journal":{"name":"UK Conference on Hypertext","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":"{\"title\":\"Searching a file system using inferred semantic links\",\"authors\":\"Deepavali Bhagwat, N. Polyzotis\",\"doi\":\"10.1145/1083356.1083372\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe Eureka, a file system search engine that takes into account the inherent relationships among files in order to improve the rankings of search results. The key idea is to automatically infer semantic links within the file system, and use the structure of the links to determine the importance of different files and essentially bias the result rankings. We discuss the inference of semantic links and describe the design of the Eureka search engine.\",\"PeriodicalId\":134809,\"journal\":{\"name\":\"UK Conference on Hypertext\",\"volume\":\"74 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"34\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"UK Conference on Hypertext\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1083356.1083372\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"UK Conference on Hypertext","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1083356.1083372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Searching a file system using inferred semantic links
We describe Eureka, a file system search engine that takes into account the inherent relationships among files in order to improve the rankings of search results. The key idea is to automatically infer semantic links within the file system, and use the structure of the links to determine the importance of different files and essentially bias the result rankings. We discuss the inference of semantic links and describe the design of the Eureka search engine.
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