{"title":"半结构化文本领域本体关系自动提取","authors":"Cheng Xiao, Dequan Zheng, Yuhang Yang, G. Shao","doi":"10.1109/IALP.2009.51","DOIUrl":null,"url":null,"abstract":"This paper presents a new method to acquire Domain-Ontology relations from semi-structured data sources. First, obtain Web documents according to the co-occurrence of concept instance and attribute value. Further, define formats of relation patterns, and extract pattern instances from Web documents, including pattern clustering and pattern combining in each cluster. Finally, relation pattern instances are applied to gain attribute values of new concept instances in Domain-Ontology. Experiments are carried out in the field of film, the rate of pattern incorrect-division and pattern leakage are respectively 0.19% and 1.31%, the highest precision of combined relation patterns reaches 85%. Experimental results demonstrate that the method developed in this paper is fairly efficient.","PeriodicalId":156840,"journal":{"name":"2009 International Conference on Asian Language Processing","volume":"248 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Automatic Domain-Ontology Relation Extraction from Semi-structured Texts\",\"authors\":\"Cheng Xiao, Dequan Zheng, Yuhang Yang, G. Shao\",\"doi\":\"10.1109/IALP.2009.51\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new method to acquire Domain-Ontology relations from semi-structured data sources. First, obtain Web documents according to the co-occurrence of concept instance and attribute value. Further, define formats of relation patterns, and extract pattern instances from Web documents, including pattern clustering and pattern combining in each cluster. Finally, relation pattern instances are applied to gain attribute values of new concept instances in Domain-Ontology. Experiments are carried out in the field of film, the rate of pattern incorrect-division and pattern leakage are respectively 0.19% and 1.31%, the highest precision of combined relation patterns reaches 85%. Experimental results demonstrate that the method developed in this paper is fairly efficient.\",\"PeriodicalId\":156840,\"journal\":{\"name\":\"2009 International Conference on Asian Language Processing\",\"volume\":\"248 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Asian Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2009.51\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2009.51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Domain-Ontology Relation Extraction from Semi-structured Texts
This paper presents a new method to acquire Domain-Ontology relations from semi-structured data sources. First, obtain Web documents according to the co-occurrence of concept instance and attribute value. Further, define formats of relation patterns, and extract pattern instances from Web documents, including pattern clustering and pattern combining in each cluster. Finally, relation pattern instances are applied to gain attribute values of new concept instances in Domain-Ontology. Experiments are carried out in the field of film, the rate of pattern incorrect-division and pattern leakage are respectively 0.19% and 1.31%, the highest precision of combined relation patterns reaches 85%. Experimental results demonstrate that the method developed in this paper is fairly efficient.