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Semantic and Machine Learning Methods for Mining Connections in the UMLS

UMLS 中挖掘连接的语义和机器学习方法

基本信息

批准号：
7498449
负责人：
CAROL FRIEDMAN
金额：
$ 15.32万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-30 至 2009-09-29
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The Unified Medical Language System (UMLS) is an invaluable resource for the biomedical community. One of the intended uses of the UMLS Metathesaurus is to support the translation of terms from a source terminology into terms in a target terminology. It is evident from the research literature on the UMLS that users generally need to perform more broader types of "translations" that involve finding terms with closest meaning to source term (mapping), finding terms that are related to source term and can serve as proxy for various functions (e.g. information retrieval, knowledge discovery) or finding target terms that satisfy some structural or semantic constraint (e.g. information theoretic distance). The methods for finding such "translations" or connections between terms in Meta (other than the case of one-to-one synonymy) are not at all clear. Previous attempts to exploit such connections have depended on either manual selection of relevant connections, or problem-specific algorithms that use expert knowledge about the relative suitability of various inter-concept relationships. We believe that machine learning techniques offer automated, generalizable approaches that are appropriate for use with the UMLS, given the large set of potential connections and the need for a problem-independent approach. We hypothesize that learning strategies that exploit the relational features, scale free properties and probabilistic dependencies of connections in the UMLS will identify meaningful inter-term relationships and that a combined approach will perform better across different problem domains when compared to any of the approaches in isolation. We will evaluate the proposed learning algorithms with training connections from a variety of problem domains in biomedicine. We will disseminate the successful algorithms via the UMLS Knowledge Source API toolkit for mining and visualizing the connections. We believe that the UMLS provides a unique fertile ground to develop novel semantic relational mining methods and advance our understanding of mining large biomedical concept graphs.

描述（由申请人提供）：统一医学语言系统 (UMLS) 是生物医学界的宝贵资源。 UMLS Metathesaurus 的预期用途之一是支持将术语从源术语翻译为目标术语中的术语。从 UMLS 的研究文献中可以明显看出，用户通常需要执行更广泛类型的“翻译”，包括查找与源术语含义最接近的术语（映射）、查找与源术语相关并可以充当各种功能（例如信息检索、知识发现）的代理的术语或查找满足某些结构或语义约束（例如信息论距离）的目标术语。在 Meta 中找到此类“翻译”或术语之间的联系（一对一同义词的情况除外）的方法根本不明确。以前利用此类连接的尝试依赖于手动选择相关连接，或者使用有关各种概念间关系的相对适用性的专业知识的针对特定问题的算法。我们相信，考虑到大量的潜在连接以及对独立于问题的方法的需求，机器学习技术提供了适合与 UMLS 一起使用的自动化、可推广的方法。我们假设，利用 UMLS 中的关系特征、无标度属性和连接的概率依赖性的学习策略将识别有意义的术语间关系，并且与任何孤立的方法相比，组合方法将在不同的问题领域中表现更好。我们将通过来自生物医学各种问题领域的训练连接来评估所提出的学习算法。我们将通过 UMLS 知识源 API 工具包传播成功的算法，以挖掘和可视化连接。我们相信，UMLS 为开发新颖的语义关系挖掘方法并增进我们对挖掘大型生物医学概念图的理解提供了独特的沃土。