{"title":"基于本体的信息检索系统","authors":"Zhang Feng","doi":"10.35940/ijrte.b3781.079220","DOIUrl":null,"url":null,"abstract":"Based on Ontology Model for the Semantic Web, this paper proposed a new Information Retrieval System. The system combined a new vector space model based on the concept semantic retrieval and retrieval. Based on the factors like semantic coincidence degree, the concept in the concept semantic model was divided into upper-lower relationship and non-upper-lower relationship for calculating the similarity value between semantic concepts. The system also introduced the Information-Gain, which effectively controlled the semantic extension. The experiment shows that the system utilized the concept semantic information fully and the computation result was reasonable.","PeriodicalId":18640,"journal":{"name":"微计算机信息","volume":"89 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Ontology Based Information Retrieval System\",\"authors\":\"Zhang Feng\",\"doi\":\"10.35940/ijrte.b3781.079220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on Ontology Model for the Semantic Web, this paper proposed a new Information Retrieval System. The system combined a new vector space model based on the concept semantic retrieval and retrieval. Based on the factors like semantic coincidence degree, the concept in the concept semantic model was divided into upper-lower relationship and non-upper-lower relationship for calculating the similarity value between semantic concepts. The system also introduced the Information-Gain, which effectively controlled the semantic extension. The experiment shows that the system utilized the concept semantic information fully and the computation result was reasonable.\",\"PeriodicalId\":18640,\"journal\":{\"name\":\"微计算机信息\",\"volume\":\"89 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"微计算机信息\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.35940/ijrte.b3781.079220\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"微计算机信息","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.35940/ijrte.b3781.079220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Based on Ontology Model for the Semantic Web, this paper proposed a new Information Retrieval System. The system combined a new vector space model based on the concept semantic retrieval and retrieval. Based on the factors like semantic coincidence degree, the concept in the concept semantic model was divided into upper-lower relationship and non-upper-lower relationship for calculating the similarity value between semantic concepts. The system also introduced the Information-Gain, which effectively controlled the semantic extension. The experiment shows that the system utilized the concept semantic information fully and the computation result was reasonable.