BIOLOGICAL KNOWLEDGE-BASED MANAGEMENT SYSTEM

生物医学研究人员正在产生大量的数据和知识， 以更快的速度。既没有现有的数据库系统，也没有现有的 框架知识表示系统具有需要的能力 支持开发大型、共享的生物信息库 信息。因此，SRI国际公司建议整合数据库 和知识表示技术来开发一种生物 具有前所未有的强大功能的知识库管理系统(KBMS) 对生物学知识进行编码。这个KBMS将提供表现力 需要代表所有复杂的生物知识，威尔 支持复杂知识库架构的演变，将使 多个用户访问大量可靠存储的信息 一种共享的方式，并将支持对此知识的推理。 我们的实现工作将建立在现有框架表示的基础上 名为西奥的系统。我们将扩展Theo，以便其底层存储 系统利用数据库管理系统来方便 包含大数(百万)的框架知识库的构建 持久化帧。此外，我们还将实施协作 协调大型数据库并发分布式开发的子系统 多个用户创建的知识库。 为了测试和验证我们的开发工作，将使用以下KBMS 在构建大型生物知识库的应用中 中间代谢。

Biomedical researchers are generating vast amounts of data and knowledge, at an accelerating pace. Neither existing database systems nor existing frame knowledge representation systems have the capabilities required to support the development of large, shared repositories of biological information. Therefore, SRI International proposes to integrate database and knowledge-representation technology to develop a biological knowledge-base management system (KBMS) with unprecedented power to en- code biological knowledge. This KBMS will provide the expressive power needed to represent biological knowledge in all its complexity, will support the evolution of complex knowledge-base schemas, will enable multiple users to access large amounts of reliably stored information in a shared fashion, and will support inference over this knowledge. Our implementation efforts will build on an existing frame representation system called THEO. We will extend THEO so that its underlying storage system utilizes a database management system to facilitate the construction of frame knowledge bases containing large numbers (millions) of persistent frames. In addition, we will implement a collaboration subsystem that coordinates concurrent distributed development of large knowledge bases by multiple users. To exercise and validate our development efforts, this KBMS will be used in an application to construct a large biological knowledge base of intermediary metabolism.