Youngchoon Park, Pan-koo Kim, F. Golshani, S. Panchanathan
{"title":"Technique for eliminating irrelevant terms in term rewriting for annotated media retrieval","authors":"Youngchoon Park, Pan-koo Kim, F. Golshani, S. Panchanathan","doi":"10.1145/500141.500247","DOIUrl":null,"url":null,"abstract":"In this paper, we present an efficient term rewriting technique that computes a degree of term to domain relevance. The proposed method resolves the problems in ontology integrated concept search. Those problems are (i) Pre-defined concept classes in ontology are not relevant to users (no proper concept class for a target annotation has not found). (ii) Too many similar concept classes are provided to a user therefore, a user may fail to choose a correct semantic class for a target annotation (ordinary users are not an expert in concept classification). The method uses sense disambiguation task for finding relevant terms for a given domain. Sense disambiguation requires term-to-term similarity measurement and term frequency measurement. For fair modeling of not observed term frequencies, discounting and redistribution model is applied. The proposed method is a compliment to our previous work presented in [13][14]. Robustness of our method is demonstrated through human judgment test that shows our method allows prediction of precise term list (overall 75% of correct prediction) that are relevant to a given domain.","PeriodicalId":416848,"journal":{"name":"MULTIMEDIA '01","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MULTIMEDIA '01","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/500141.500247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we present an efficient term rewriting technique that computes a degree of term to domain relevance. The proposed method resolves the problems in ontology integrated concept search. Those problems are (i) Pre-defined concept classes in ontology are not relevant to users (no proper concept class for a target annotation has not found). (ii) Too many similar concept classes are provided to a user therefore, a user may fail to choose a correct semantic class for a target annotation (ordinary users are not an expert in concept classification). The method uses sense disambiguation task for finding relevant terms for a given domain. Sense disambiguation requires term-to-term similarity measurement and term frequency measurement. For fair modeling of not observed term frequencies, discounting and redistribution model is applied. The proposed method is a compliment to our previous work presented in [13][14]. Robustness of our method is demonstrated through human judgment test that shows our method allows prediction of precise term list (overall 75% of correct prediction) that are relevant to a given domain.