Design of optimization techniques and software architectures for description logic reasoners

描述逻辑推理机的优化技术和软件架构设计

• 批准号: 261562-2013
• 负责人: Haarslev, Volker
• 金额: $ 2.19万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525283
• 关键词: Design; optimization; techniques; software; architectures

The proposed research program is mainly concerned with the design and empirical evaluation of optimization techniques for description logic (DL) reasoners. Over the last decade DL reasoning has gained quite some attention from the semantic web community because the Web Ontology Language, OWL, and its sequel OWL 2 are based on description logics. The DL subset of OWL 2 is a syntactic variant of a well-known and very expressive DL. Roughly speaking DL knowledge is described using concepts, roles, and individuals that can be combined with various constructors. Concepts describe sets of individuals with common properties and roles specify binary relationships between individuals. The concept satisfiability problem for OWL 2 DL is known to be 2-NExpTime-complete. Most reasoners are based on tableau (proof) procedures. The implementation of these calculi requires highly sophisticated optimization techniques due to the structure of the calculi and the inherent time complexity of the inference problems. A large set of tableau optimization techniques needs to be applied to speed up provided inference services and make DL reasoning feasible in practical applications.

建议的研究计划主要涉及描述逻辑（DL）推理机的优化技术的设计和实证评估。在过去的十年中，DL推理已经得到了语义Web社区的关注，因为Web本体语言OWL及其续集OWL 2是基于描述逻辑的。OWL 2的DL子集是一个著名的、表达能力很强的DL的语法变体。粗略地说，深度学习知识是使用概念、角色和个体来描述的，这些概念、角色和个体可以与各种构造器相结合。概念描述具有共同属性的个体集合，角色指定个体之间的二元关系。OWL 2 DL的概念可满足性问题是2-NExpTime-完全的。大多数推理机都是基于tableau（证明）过程的。由于演算的结构和推理问题的固有时间复杂性，这些演算的实现需要高度复杂的优化技术。需要应用大量的tableau优化技术来加速所提供的推理服务，并使DL推理在实际应用中可行。