{"title":"基于大众分类法的网络搜索结果k近邻分类方法","authors":"C. Yeung, Nicholas Gibbins, N. Shadbolt","doi":"10.1109/WIIAT.2008.269","DOIUrl":null,"url":null,"abstract":"Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies\",\"authors\":\"C. Yeung, Nicholas Gibbins, N. Shadbolt\",\"doi\":\"10.1109/WIIAT.2008.269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.\",\"PeriodicalId\":393772,\"journal\":{\"name\":\"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WIIAT.2008.269\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIIAT.2008.269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies
Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.