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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

基本信息

批准号：
8138564
负责人：
Trevor Cohen
金额：
$ 18万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2013-09-29
项目状态：
已结题

项目摘要

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）：从生物医学文献中进行有效的信息检索对于支持循证临床实践至关重要。然而，研究表明，临床医生很难产生足够具体的查询使用现有的接口，电子信息资源。由于生物医学研究文献的快速增长，有必要开发工具，使临床医生和研究人员能够找到和检索感兴趣的文件。在信息检索中，向量空间模型是一种将文档表示为高维空间中的向量的模型。然而，由于该模型基于术语（或模型变体中的概念）对文档进行索引，这限制了可以查询这些文档的特异性。在我们最近的研究中，我们开发了基于预测的语义索引（PSI），一种基于向量的模型，它以SemRep系统从MEDLINE中提取的对象-关系-对象三元组（或预测）的形式将知识编码到向量空间中。在拟议的研究中，我们将开发和评估一个新的信息检索模型的基础上PSI。该模型将允许使用概念和关系搜索文档，以回答诸如“什么用于治疗结核病”之类的特定问题。该模型代表了信息检索研究的一个新方向，我们的假设是，基于预测的文档表示将使查询的规范比现有模型更精确。为了检验这一假设，将使用OHSUMED测试集对模型进行评估，并使用标准性能指标与传统向量空间模型进行比较。