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扩展到人类以及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)以及将信息编译成(通常是分布式的)形式本体或知识库。Web Ontology Language(OWL)于2004年被批准为W3C推荐标准。它是对语义网倡议的回应，已成为许多科学界以及公司和政府知识表示的事实上标准。OWL部分基于一系列称为描述逻辑的知识表示形式。描述逻辑的吸引力在于可以立即获得高度优化的推理器，提供与支持语义网相关的广泛自动推理服务。描述逻辑推理器通常基于所谓的Tableau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并支持持久性。这一目标归功于语义网社区的最新发展，其中(简单描述逻辑)推理已被应用于由多达数百万个断言组成的非常大的本体。挑战是提供持久性并确保可伸缩性，即使需要复杂的推理。这激发了设计和评估Tableau优化技术的需要，这些技术支持对持久的和几乎无限的本体进行可伸缩的推理。第二个目标是开发新的优化技术，用于描述逻辑构造函数的组合，例如数量限制、名词性和倒置角色，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.