Representing and Acquiring Knowledge of Genome Regulatio

描述（由申请人提供）： 分子生物学的知识由关于分子实体关系的断言组成，这些断言由描述这些断言何时何地适用的上下文限定。分子生物学的绝大多数知识都存在于主要的研究文献中，目前只有一小部分知识可以通过结构良好的数据库获得。这是一个开发自动知识提取技术的试点项目。我们将使用造血中基因表达的调节作为测试域。知识获取将通过多阶段过程完成：解析文档和句子结构，识别已知生物实体的名称，并将句子与基于动词的模板进行匹配以捕获断言（例如;A绑定B;或;A包含B; A调节B;）和介词模板以捕获这些断言应用的上下文。将利用生物信息学、数据库、信息科学和计算语言学方面的专家，采用多学科方法。这个项目的四个独特的方面是定义的分子生物学背景下的多维描述，使用介词模板和层次化的文档结构，以捕获和推理上下文，特定领域的解析技术的发展和使用的概率表示明确表示在XML整个文本处理，解析，知识获取和信息集成。

DESCRIPTION (provided by applicant): Knowledge in molecular biology consists of assertions about the relationship of molecular entities qualified by context which describes when and where those assertions apply. The vast majority of knowledge in molecular biology resides in the primary research literature, and only a small fraction of this knowledge is currently accessible through well-structured databases. This is a pilot project to develop automated knowledge extraction technology. We will use the regulation of gene expression in hematopoiesis as a test domain. Knowledge acquisition will be accomplished through a multi-stage process: parsing the document and sentence structure, recognizing the names of known biological entities and matching sentences to verb based templates to capture assertions (e.g. ;A binds B; or ;A contains B; A regulates B;) and preposition templates to capture context in which these assertions apply. A multi-disciplinary approach will be used drawing on experts in bioinformatics, databases, information science and computational linguistics. Four unique aspects of this project are the definition of a multi-dimensional description of molecular biological context, the use of preposition templates and hierarchical document structure to capture and make inference on context, the development of domain specific parsing techniques and the use of probabilistic representations explicitly represented in XML throughout text processing, parsing, knowledge acquisition and information integration.