Reasoning Infrastructure for Ontologies and Instances

本体通过提供术语词汇表以及对术语含义和它们之间关系的正式和计算机可处理的描述来帮助人类和计算机应用程序进行交流。它们在下一代万维网(称为语义网)中扮演着重要角色，它们被用来描述Web资源的内容，目的既是为了改进对人类用户的搜索，也是为了使计算机程序更容易地利用Web上可用的大量信息。本体也被广泛用于定义医学、生物学和其他科学学科中使用的专业词汇。本体通常是由人类专家开发的，但即使对专家来说，定义所有相关术语也是一项困难和耗时的工作。因此，提供支持本体设计者的智能工具是至关重要的。因此，许多本体语言，包括OWL(用于语义Web本体的标准语言)，都是基于描述逻辑(DLS)的。这提供了语言含义的正式规范，并允许工具使用自动推理系统，例如，检查描述之间的交互不会导致逻辑矛盾。当本体被部署到应用中时，也需要推理系统，其中本体被用来回答使用本体中定义的术语的查询。然而，本体在上述应用中的核心作用带来了对表达能力和推理支持的要求，这超出了现有本体语言和推理系统的能力。例如，OWL不能表达一个人父亲的兄弟也是他们的叔叔这一事实，即使对于OWL来说，推理也非常困难：现有的推理系统通常难以处理许多现实应用中需要的非常大的本体。这项建议中描述的研究旨在弥合需求和能力之间的鸿沟；其最终目标是开发一个推理系统，在可伸缩性和所支持的本体语言的表达能力方面，显著扩展当前技术的水平。研究计划将由三个主要部分组成：第一个部分将专注于关于本体中描述的领域结构的推理，并旨在为使用现有本体语言标准和提议的扩展为应用程序提供推理支持所需的表达描述逻辑开发高度优化的类推理。第二个目标是将DL推理机与数据库相结合，以便为使用本体中的术语描述的大量数据提供可伸缩的推理。第三个目标是与本体开发者和用户合作，以使用来自他们的本体和应用程序的数据，特别是来自基因本体(和其他大型生物医学本体)的数据和大量用基因本体术语标注的基因产品数据来评估上述系统的有效性。

Ontologies help both humans and computer applications to communicate by providing a vocabulary of terms together with formal and computer-processable descriptions of their meanings and the relationships between them. They play a major role in the next-generation World Wide Web (known as the Semantic Web), where they are used to describe the content of Web resources, with the aim of both improving search for human users and making it easier for computer programs to exploit the vast range of information that is available on the Web. Ontologies are also widely used to define specialised vocabularies for use in medicine, biology and other scientific disciplines.Ontologies are usually developed by human experts, but even for experts the job of defining all the relevant terms is a difficult and time consuming one. It is therefore essential to provide intelligent tools that support ontology designers. For this reason, many ontology languages, including OWL (the standard language used for Semantic Web ontologies), are based on Description Logics (DLs). This provides a formal specification of the meaning of the language and allows tools to use automated reasoning systems, e.g., to check that interactions between descriptions do not lead to logical contradictions. Reasoning systems are also needed when ontologies are deployed in applications, where they are used, e.g., to answer queries that use terms defined in an ontology.The central role of ontologies in the above mentioned applications brings with it, however, requirements for expressive power and reasoning support which are beyond the capabilities of existing ontology languages and reasoning systems. For example, OWL cannot express the fact that the brother of a person's father is also their uncle, and even for OWL, reasoning is very hard: existing reasoning systems often have difficulties dealing with the very large ontologies that are needed in many realistic applications. The research described in this proposal aims at bridging this gulf between requirements and capabilities; its ultimate goal is the development of a reasoning system that significantly extends the current state of the art, with respect to both scalability and the expressive power of the ontology language supported.The research programme will be made up of three main strands: The first strand will focus on reasoning about the structure of the domain as described in an ontology, and aims to develop a highly optimised class reasoner for the expressive description logics needed to provide reasoning support for applications using existing ontology language standards and proposed extensions. The second strand aims to combine a DL reasoner with a database in order to provide scalable reasoning for large volumes of data that are are described using terms from an ontology. The third strand aims at collaborating with ontology developers and users in order to evaluate the effectiveness of the above systems using data from their ontologies and applications, in particular the Gene Ontology (and other large biomedical ontologies) and large volumes of gene product data annotated with terms from the Gene Ontology.