Reasoning Infrastructure for Ontologies and Instances

本体通过提供术语的词汇表以及对它们的含义和它们之间的关系的正式的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通信。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，目的是改善人类用户的搜索，并使计算机程序更容易利用Web上的大量信息。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。本体通常由人类专家开发，但即使对于专家来说，定义所有相关术语的工作也是困难和耗时的。因此，它是必不可少的，以提供智能工具，支持本体设计师。出于这个原因，许多本体语言，包括OWL（用于语义Web本体的标准语言），都基于描述逻辑（DL）。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，检查描述之间的相互作用不会导致逻辑矛盾。当本体被部署在应用程序中时，也需要推理系统，在应用程序中使用本体，例如，然而，本体在上述应用中的中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无法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所需的非常大的本体。本提案中所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，该系统在可扩展性和所支持的本体语言的表达能力方面大大扩展了当前的技术水平。研究计划将由三个主要部分组成：第一条链将集中于对本体中描述的域的结构进行推理，的目标是开发一个高度优化的类推理器的表达描述逻辑需要提供推理支持的应用程序使用现有的本体语言标准，并提出extensions.第二股旨在将DL推理机与数据库联合收割机结合，以便为使用本体中的术语描述的大量数据提供可扩展的推理。第三链旨在与本体开发者和用户合作，以便使用来自其本体和应用的数据来评估上述系统的有效性，特别是基因本体（和其他大型生物医学本体）和用来自基因本体的术语注释的大量基因产物数据。

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.