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On Optimization of Predictions in Ontology-Driven Situation Awareness

机译：论本体驱动态势感知的预测优化

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摘要

Systems supporting situation awareness in large-scale control systems, such as, e.g., encountered in the domain of road traffic management, pursue the vision of allowing human operators prevent critical situations. Recently, approaches have been proposed, which express situations, their constituting objects, and the relations in-between (e.g., road works causing a traffic jam), by means of domain-independent ontologies, allowing automatic prediction of future situations on basis of relation derivation. The resulting vast search space, however, could lead to unacceptable runtime performance and limited expressiveness of predictions. In this paper, we argue that both issues can be remedied by taking inherent characteristics of objects into account. For this, an ontology is proposed together with optimization rules, allowing to exploit such characteristics for optimizing predictions. A case study in the domain of road traffic management reveals that search space can be substantially reduced for many real-world situation evolutions, and thereby demonstrates the applicability of our approach.
机译：支持大规模控制系统中的情况感知的系统，例如在道路交通管理领域中遇到的系统，追求的愿景是允许操作员预防紧急情况。近来，已经提出了方法，该方法借助于与领域无关的本体来表达状况，其构成对象以及它们之间的关系（例如，道路工程引起交通拥堵），从而允许基于关系自动预测未来状况。推导。但是，由此产生的巨大搜索空间可能导致运行时性能无法接受，并且预测的表达能力有限。在本文中，我们认为可以通过考虑对象的固有特性来解决这两个问题。为此，提出了本体以及优化规则，从而可以利用这些特征来优化预测。道路交通管理领域的案例研究表明，对于许多现实情况的演变，搜索空间可以大大减少，从而证明了我们方法的适用性。

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