Stephan Bloehdorn, P. Cimiano, A. Hotho, Steffen Staab
{"title":"基于本体的文本挖掘框架","authors":"Stephan Bloehdorn, P. Cimiano, A. Hotho, Steffen Staab","doi":"10.21248/jlcl.20.2005.70","DOIUrl":null,"url":null,"abstract":"Structuring of text document knowledge frequently appears either by ontologies and metadata or by automatic (un-)unsupervised text categorization. This paper describes our integrated framework OTTO (OnTology-based Text mining framewOrk). OTTO uses text mining to learn the target ontology from text documents and uses then the same target ontology in order to improve the effectiveness of both supervised and unsupervised text categorization approaches.","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2005-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":"{\"title\":\"An Ontology-based Framework for Text Mining\",\"authors\":\"Stephan Bloehdorn, P. Cimiano, A. Hotho, Steffen Staab\",\"doi\":\"10.21248/jlcl.20.2005.70\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structuring of text document knowledge frequently appears either by ontologies and metadata or by automatic (un-)unsupervised text categorization. This paper describes our integrated framework OTTO (OnTology-based Text mining framewOrk). OTTO uses text mining to learn the target ontology from text documents and uses then the same target ontology in order to improve the effectiveness of both supervised and unsupervised text categorization approaches.\",\"PeriodicalId\":346957,\"journal\":{\"name\":\"LDV Forum\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"63\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"LDV Forum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21248/jlcl.20.2005.70\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"LDV Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21248/jlcl.20.2005.70","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Structuring of text document knowledge frequently appears either by ontologies and metadata or by automatic (un-)unsupervised text categorization. This paper describes our integrated framework OTTO (OnTology-based Text mining framewOrk). OTTO uses text mining to learn the target ontology from text documents and uses then the same target ontology in order to improve the effectiveness of both supervised and unsupervised text categorization approaches.
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