{"title":"使用本体和最近邻聚类的概念摘要","authors":"Elaheh Gavagsaz, Mahmoud Naghibzadeh, Mehrdad Jalali","doi":"10.1109/STAIR.2011.5995756","DOIUrl":null,"url":null,"abstract":"Conceptual summarization aims to provide a database which comprises an abstraction of the entire document content. To effectively provide conceptual summarization, we have presented an approach that is used for conceptual querying. The approach is based on utilizing an ontology for similarity measure between concepts and the nearest neighborhood clustering algorithm for concepts clustering. The results show an improvement in the runtime and tolerant as regards noise.","PeriodicalId":376671,"journal":{"name":"2011 International Conference on Semantic Technology and Information Retrieval","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Conceptual summarization using ontologies and nearest neighborhood clustering\",\"authors\":\"Elaheh Gavagsaz, Mahmoud Naghibzadeh, Mehrdad Jalali\",\"doi\":\"10.1109/STAIR.2011.5995756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conceptual summarization aims to provide a database which comprises an abstraction of the entire document content. To effectively provide conceptual summarization, we have presented an approach that is used for conceptual querying. The approach is based on utilizing an ontology for similarity measure between concepts and the nearest neighborhood clustering algorithm for concepts clustering. The results show an improvement in the runtime and tolerant as regards noise.\",\"PeriodicalId\":376671,\"journal\":{\"name\":\"2011 International Conference on Semantic Technology and Information Retrieval\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Semantic Technology and Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STAIR.2011.5995756\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Semantic Technology and Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STAIR.2011.5995756","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Conceptual summarization using ontologies and nearest neighborhood clustering
Conceptual summarization aims to provide a database which comprises an abstraction of the entire document content. To effectively provide conceptual summarization, we have presented an approach that is used for conceptual querying. The approach is based on utilizing an ontology for similarity measure between concepts and the nearest neighborhood clustering algorithm for concepts clustering. The results show an improvement in the runtime and tolerant as regards noise.
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