Text mining and ontologies in biomedicine: Making sense of raw text
Text mining and ontologies in biomedicine: Making sense of raw text
复制标题
DOI：
10.1093/bib/6.3.239
复制
发表时间：
2005-09-01
影响因子：
9.5
通讯作者：
Kumar, A
中科院分区：
文献类型：
--
作者：
Spasic, I;Ananiadou, S;Kumar, A
生物医学文献的数量正在以如此快的速度增加，以至于如果没有文本挖掘，就很难定位、检索和管理报告的信息，文本挖掘的目的是自动提取信息、提取事实、发现隐含链接并生成与用户需求相关的假设。本体作为概念模型，为文本信息的语义表示提供了必要的框架。文本和本体之间的主要联系是术语，它将术语映射到特定领域的概念。本文总结了不同的方法，其中本体已被用于文本挖掘在生物医学中的应用。
The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.