Jiangnan Qiu, Yongxiang Yao, Yanzhang Wang, Xuehua Wang
{"title":"Research on E-Government Knowledge Navigation System Based on XTM","authors":"Jiangnan Qiu, Yongxiang Yao, Yanzhang Wang, Xuehua Wang","doi":"10.1109/WI-IATW.2006.111","DOIUrl":null,"url":null,"abstract":"Against problem on current government portal navigation system is mostly based on keyword. An XTM-based e-government knowledge navigation system model was proposed, and a XTM based association and occurrences measurement mechanism is imported to the system. Especially based on analyses of topic relative semantic structure, a tree-structure semantic architecture is extracted from XTM by dividing the type of topic association. Semantics relatedness is introduced in the article to expand the second level association and calculate the degree of topic relative. The results show that the system has a good effectiveness and efficiency of navigation","PeriodicalId":358971,"journal":{"name":"2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IATW.2006.111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Against problem on current government portal navigation system is mostly based on keyword. An XTM-based e-government knowledge navigation system model was proposed, and a XTM based association and occurrences measurement mechanism is imported to the system. Especially based on analyses of topic relative semantic structure, a tree-structure semantic architecture is extracted from XTM by dividing the type of topic association. Semantics relatedness is introduced in the article to expand the second level association and calculate the degree of topic relative. The results show that the system has a good effectiveness and efficiency of navigation