{"title":"使用谷歌和术语依赖的自动分类法提取","authors":"M. Makrehchi, M. Kamel","doi":"10.1109/WI.2007.26","DOIUrl":null,"url":null,"abstract":"An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Automatic Taxonomy Extraction Using Google and Term Dependency\",\"authors\":\"M. Makrehchi, M. Kamel\",\"doi\":\"10.1109/WI.2007.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.\",\"PeriodicalId\":192501,\"journal\":{\"name\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"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.2007.26\",\"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.2007.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Taxonomy Extraction Using Google and Term Dependency
An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.