Search strategies for reasoning about spatial ontologies

J. Pais, C. Pinto-Ferreira
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引用次数: 6

Abstract

The utilization of spatial ontologies to representing and reasoning about real-world problems exhibits some advantages when compared with traditional approaches. Namely reification, flexibility, efficiency and process understanding are considerably improved. However, the search mechanism, which underlies the reasoning process, always has exponential complexity, mainly because real-world problems produce models with high branching factors and solution depths. Therefore, the study and implementation of control strategies and cooperation among them are essential to meet the challenge of search complexity reduction. In particular, improvements on two fundamental steps of the process-successor generation and state completion can reduce drastically the search effort. In this paper, some search strategies are introduced, such as the utilization factor and the use of several lists of open nodes. Some classical approaches as sub-goaling and heuristic search are applied to the problem of controlling the process of reasoning about spatial ontologies. The other aim of this paper is to discussing some kinds of cooperation among the previous search strategies to reaching a search complexity reduction. The proposed search strategies were incorporated in a reasoner implemented in Prolog, whose performance has shown drastical improvements.
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空间本体推理的搜索策略
与传统方法相比,利用空间本体来表示和推理现实世界的问题显示出一些优势。即具体化,灵活性,效率和过程的理解大大提高。然而,作为推理过程基础的搜索机制总是具有指数复杂度,这主要是因为现实世界的问题产生具有高分支因子和解深度的模型。因此,控制策略的研究与实现以及控制策略之间的协同是解决搜索复杂度降低问题的关键。特别是,对过程后继生成和状态完成两个基本步骤的改进可以大大减少搜索工作。本文介绍了一些搜索策略,如利用因子和多个开放节点列表的使用。子目标和启发式搜索等经典方法应用于空间本体推理过程的控制问题。本文的另一个目的是讨论几种搜索策略之间的合作,以达到降低搜索复杂度的目的。提出的搜索策略被整合到Prolog中实现的推理器中,该推理器的性能得到了显著改善。
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