Minlie Huang, Xiaoyan Zhu, Shilin Ding, Hao Yu, Ming Li
{"title":"ONBRIRES: Ontology-Based Biological Relation Extraction System","authors":"Minlie Huang, Xiaoyan Zhu, Shilin Ding, Hao Yu, Ming Li","doi":"10.1142/9781860947292_0036","DOIUrl":null,"url":null,"abstract":"Automated discovery and extraction of biological relations from online documents, particularly MEDLINE texts, has become essential and urgent because such literature data are accumulated in a tremendous growth. In this paper, we present an ontology-based framework of biological relation extraction system. This framework is unified and able to extract several kinds of relations such as gene-disease, gene-gene, and protein-protein interactions etc. The main contributions of this paper are that we propose a two-level pattern learning algorithm, and organize patterns hierarchically.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"55 1","pages":"327-336"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... Asia-Pacific bioinformatics conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9781860947292_0036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Automated discovery and extraction of biological relations from online documents, particularly MEDLINE texts, has become essential and urgent because such literature data are accumulated in a tremendous growth. In this paper, we present an ontology-based framework of biological relation extraction system. This framework is unified and able to extract several kinds of relations such as gene-disease, gene-gene, and protein-protein interactions etc. The main contributions of this paper are that we propose a two-level pattern learning algorithm, and organize patterns hierarchically.
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