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arXiv 1209.5571cs.LOcs.AI
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基于描述逻辑的时态概念数据建模方法

A Cookbook for Temporal Conceptual Data Modelling with Description Logics

Free University of Bozen-Bolzano(博尔扎诺自由大学);Birkbeck, University of London(伦敦大学伯贝克学院)

Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael Zakharyaschev

摘要:

我们设计了适用于时态概念数据模型推理的时态描述逻辑,并研究了它们的计算复杂性。我们的形式主义是基于DL-Lite逻辑与三种类型的概念包含(范围从原子概念包含和不相交的完整布尔),以及基数约束和角色包含。在时间维度上,它们捕获未来和过去的时间操作符的概念,灵活和刚性的角色,操作符的“总是”和“一些时间”的角色,数据断言的特定时刻的时间和全球概念的包容性。这些逻辑被解释为对象域和时间流(Z,<)的笛卡尔积,满足恒定域假设。我们证明,我们的时间描述逻辑(它可以捕捉生命周期基数和定性或定量的进化约束)最具表现力的结果是不可判定的。然而,通过省略概念/角色上的一些时态运算符或通过限制概念包含的形式,我们获得了复杂度介于PSpace和NLogSpace之间的逻辑。这些积极的结果是通过减少到命题时态逻辑的各种子句片段,这开辟了一条道路,采用命题或一阶时态证明推理的时间数据模型。

英文摘要:

We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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