Encoding Semantic Knowledge in Vector Space for Biomedical Information

在生物医学信息的向量空间中编码语义知识

• 批准号: 7977263
• 负责人: Trevor Cohen
• 金额: $ 22.15万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7977263
• 关键词: Accounting; Biomedical Research; Collection; Computer software; Data Set; Databases; Derivation procedure; Dimensions; Electronics; Elements; Estrogen Replacement Therapy; Evaluation; Goals; Haloperidol; Information Resources; Information Retrieval; Information Retrieval Systems; Judgment; Knowledge; Literature; MEDLINE; Methods; Metric; Modeling; Object Relations; Performance; Publishing; Research; Research Personnel; Retrieval; Schizophrenia; Semantics; Space Models; Specificity; System; Techniques; Testing; Triplet Multiple Birth; Tuberculosis; Variant; Weight; Work; base; clinical practice; evidence base; improved; indexing; interest; isoniazid; malignant breast neoplasm; open source; software development; tool development; vector

DESCRIPTION (provided by applicant): Effective information retrieval from the biomedical literature is essential to supporting evidence-based clinical practice. However, research suggests that clinicians have difficulty generating sufficiently specific queries using existing interfaces to electronic information resources. On account of the rapid proliferation of the biomedical research literature, there is a need for the development of tools to enable clinicians and researchers to find and retrieve documents of interest. The vector space model, in which documents are represented as vectors in a high-dimensional space, is well established in information retrieval. However, as this model indexes documents on the basis of terms (or concepts in variants of the model), this limits the specificity with which these documents can be queried. In our recent research, we have developed Predication-based Semantic Indexing (PSI), a vector-based model which encodes knowledge in the form of object-relation-object triplets (or predications) extracted from MEDLINE by the SemRep system, into vector space. In the proposed research we will develop and evaluate a new model of information retrieval based on PSI. This model will enable searching for documents using concepts and relations, in order to answer specific questions such as "what is used to treat Tuberculosis". This model represents a new direction in information retrieval research, and our hypothesis is that document representations based on predications will enable the specification of queries that are more precise than are possible with existing models. To test this hypothesis, the model will be evaluated using the OHSUMED test set, and compared to the traditional vector space model using standard performance metrics.

描述（由申请人提供）：从生物医学文献中检索有效的信息对于支持基于证据的临床实践至关重要。但是，研究表明，临床医生很难使用现有界面来产生足够特定的查询来电子信息资源。由于生物医学研究文献的快速扩散，需要开发工具来使临床医生和研究人员能够找到和检索感兴趣的文件。向量空间模型在高维空间中表示为矢量，在信息检索中已良好确定。但是，由于该模型根据术语（或模型的变体中的概念）索引文档，因此这限制了这些文档可以查询的特殊性。在我们最近的研究中，我们开发了基于鉴定的语义索引（PSI），这是一个基于向量的模型，该模型以Semrep System从MEDLINE从MEDLINE提取的对象凝聚 - 对象三重态（或谓词）的形式编码知识。在拟议的研究中，我们将根据PSI开发和评估新的信息检索模型。该模型将通过概念和关系搜索文档，以回答特定问题，例如“用于治疗结核病的内容”。该模型代表了信息检索研究的一个新方向，我们的假设是基于谓词的文档表示形式将使查询的规范比现有模型更精确的查询。为了检验该假设，将使用OH耗费的测试集对模型进行评估，并将使用标准性能指标与传统矢量空间模型进行比较。