Protege: An Ontology-Development Platform for Biomedical Scientists

描述（由申请人提供）：定义应用领域中的实体以及它们之间的关系的本体的工程已经成为现代生物医学工作的关键。 本体帮助人类和计算机管理数量激增的数据。 需要注释，检索和集成高吞吐量的数据集，处理自然语言，并建立决策支持系统，许多社区的调查工作建立大型本体。Protégé系统已成为一个不可或缺的开源资源，为一个巨大的国际科学家社区，支持开发，维护和使用的本体和电子知识库的生物医学研究人员无处不在。Protégé的注册用户从2002年的3，500人增加到撰写本文时的195，000多人。然而，到目前为止，本体在生物医学中的使用受到本体开发工具复杂性的限制，这往往使许多生物医学科学家无法访问本体。 在本提案中，我们将开发新的方法和工具，大大降低本体开发的门槛，扩展Protégé，在整个本体生命周期中提供直观和用户友好的本体获取方法。 我们的计划包括五个具体目标。 首先，我们将开发的方法，使初始规范的本体术语在一个非正式的方式，使用列表和图表。 科学家们将能够开始建模他们的领域，而不必考虑正式的本体论区别。第二，我们将提供直观的，易于使用的工具，本体规范，这将有助于开发人员，因为他们开始形式化他们的模型。 第三，我们将跟踪的要求，本体必须解决和开发新的方法来评估这些要求的基础上的本体覆盖。 第四，对于本体本身具有复杂的内部结构，不能完全使用简单的本体结构表示，我们将开发的方法，将创建模板，涵盖在本体的常规结构。然后，科学家将能够根据这些模板填写表格，Protégé在后台生成相应的逻辑结构。 第五，我们将继续扩大和支持蓬勃发展的Protégé用户社区，因为它扩展到包括生物医学科学家，他们现在将能够构建本体来支持他们的数据驱动的研究和发现。

DESCRIPTION (provided by applicant): The engineering of ontologies that define the entities in an application area and the relationships among them has become essential for modern work in biomedicine. Ontologies help both humans and computers to manage burgeoning numbers of data. The need to annotate, retrieve, and integrate high-throughput data sets, to process natural language, and to build systems for decision support has set many communities of investigators to work building large ontologies. The Protégé system has become an indispensable open-source resource for an enormous internationa community of scientists-supporting the development, maintenance, and use of ontologies and electronic knowledge bases by biomedical investigators everywhere. The number of registered Protégé users has grown from 3,500 in 2002 to more than 195,000 users as of this writing. To date, however, the use of ontologies in biomedicine has been limited by the complexity of the ontology-development tools, which often make ontologies inaccessible to many biomedical scientists. In this proposal, we will develop new methods and tools that will significantly lower the barrier of entry for ontology development, expanding Protégé to provide intuitive and user-friendly ontology-acquisition methods throughout the ontology lifecycle. Our plan entails five specific aims. First, we will develop methods that enable initial specification of ontology terms in an informal manner, using lists and diagrams. Scientists will be able to start modeling their domain without having to think in terms of formal ontological distinctions. Second, we will provide intuitive, easy-to-use tools for ontology specification that will aid developers as they start to formalize their models. Third, we will track the requirements that an ontology must address and develop novel methods for evaluating ontology coverage based on these requirements. Fourth, for ontologies that inherently have complex internal structure that cannot be represented fully using only simple ontology constructs, we will develop methods that will create templates covering regular structures in the ontology. Scientists will then be able to fill out forms based o these templates, with Protégé generating the corresponding logical structure in the background. Fifth, we will continue to expand and support the thriving Protégé user community, as it expands to include the biomedical scientists who will now be able to build the ontologies to support their data-driven research and discoveries.