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

• 批准号: 7299922
• 负责人: JAMES J CIMINO
• 金额: $ 18.11万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/7299922
• 关键词: Algorithms; Communities; Complex; Data; Data Set; Dependency; Disease; Graph; Healthcare; Information Retrieval; Knowledge; Language; Learning; Literature; Machine Learning; Manuals; Maps; Medical; Methods; Mining; Nature; Ontology; Organism; Pathway interactions; Property; Proxy; Relative (related person); Research; Resources; Retrieval; Sampling; Semantics; Social Network; Solutions; Source; System; Techniques; Terminology; Training; Translating; Translations; Unified Medical Language System; Work; base; biomedical resource; concept; interest; metathesaurus; microbial alkaline proteinase inhibitor; novel; success

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源术语中的术语翻译为目标术语中的术语。从关于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”，包括寻找与源词语含义最接近的词语(映射)，寻找与源词语相关并可用作各种功能的代理的词语(例如，信息检索、知识发现)，或者寻找满足某些结构或语义约束(例如，信息理论距离)的目标词语。在Meta中寻找这种“翻译”或术语之间的连接的方法(除了一对一同义词的情况外)根本不清楚。以前开发这种联系的尝试要么依赖于相关联系的手动选择，要么依赖于使用关于各种概念间关系的相对适宜性的专家知识的特定问题算法。我们认为，考虑到大量的潜在联系和需要与问题无关的方法，机器学习技术提供了适合于UMLS使用的自动化、可推广的方法。我们假设，利用UMLS中连接的关系特征、无标度特性和概率依赖关系的学习策略将识别有意义的术语间关系，并且与单独使用任何一种方法相比，组合方法将在不同的问题领域中执行得更好。我们将使用生物医学中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 Knowledge Source 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为开发新的语义关系挖掘方法提供了独特的肥沃土壤，并提高了我们对挖掘大型生物医学概念图的理解。