Scalable description logic reasoning architectures for the semantic web

语义网将万维网从为人类消费而设计的数据和文档的集合（通常很难找到和使用）扩展为人类和计算机程序可以理解和非常方便地使用的知识集合。语义网中可用的知识依赖于广泛传播的信息（例如，网页集合）和将信息汇编成（通常是分布式的）正式本体或知识库。Web本体语言（OWL）在2004年被批准为W3C推荐标准。它是对语义网倡议的回应，并且已经成为许多科学团体、公司和政府的知识表示的事实上的标准。OWL部分地基于一组称为描述逻辑的知识表示形式。描述逻辑的吸引力在于高度优化的推理器的即时可用性，它提供了广泛的与支持语义web相关的自动推理服务。描述逻辑推理器通常基于所谓的表过程，由于其固有的运行时复杂性，需要高度复杂的优化技术。该建议的第一个目标是实现更好的推理可伸缩性和支持持久性。这个目标是由于语义web社区最近的发展，其中（简单描述逻辑）推理已经应用于由多达数百万个断言组成的非常大的本体。挑战在于即使需要复杂的推理，也要提供持久性和确保可伸缩性。这激发了设计和评估支持持久和几乎无限本体的可扩展推理的表优化技术的需求。第二个目标是为描述逻辑构造函数（如数量限制、标称和逆角色）的组合开发新的优化技术，因为已知的技术是不够的。

The semantic web extends the WWW from a collection of data and documents, that are designed for human consumption and are often hard to find and use, into a collection of knowledge that is understandable and very convenient to use by humans as well as computer programs. The knowledge available in the semantic web depends on wide-spread information (e.g., collections of web pages) and on the compilation of information into (often distributed) formal ontologies or knowledge bases. The Web Ontology Language (OWL) was approved in 2004 as a W3C recommendation. It is a response to the semantic web initiative and has become a de-facto standard in knowledge representation for many scientific communities as well as companies and governments. OWL is partially based on a family of knowledge representation formalisms called description logics. The appeal of description logics is the immediate availability of highly optimized reasoners offering a wide range of automatic inference services relevant for supporting the semantic web. Description logic reasoners are usually based on so-called tableau procedures requiring highly sophisticated optimization techniques due to their inherent runtime complexity. The first objective of this proposal is to achieve better reasoning scalability and to support persistency. This goal is due to recent developments in the semantic web community where (simple description logic) reasoning has been applied to very large ontologies consisting of up to millions of assertions. The challenge is to provide persistency and ensure scalability even if complex reasoning is required. This motivates the need to design and evaluate tableau optimization techniques which support scalable reasoning on persistent and virtually unlimited ontologies. The second objective is to develop novel optimization techniques for combinations of description logic constructors such as number restrictions, nominals, and inverse roles because known techniques are insufficient.