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Derivation of ontological relations using formal methods in a situation awareness scenario

机译:在情境感知场景中使用形式化方法推导本体关系

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This paper describes a case study of relation derivation within the context of situation awareness. First we present a scenario in which inputs are supplied by a simulated Level 1 system. The inputs are events annotated with terms from an ontology for situation awareness. This ontology contains concepts used to represent and reason about situations. The ontology and the annotations of events are represented in DAML and Rule-ML and then systematically translated to a formal method language called MetaSlang. Having all information expressed in a formal method language allows us to use a theorem prover, SNARK, to prove that a given relationship among the Level 1 objects holds (or that it does not hold). The paper shows a proof of concept that relation derivation in situation awareness can be done within a formal framework. It also identifies bottlenecks associated with this approach, such as the issue of the large number of potential relations that may have to be considered by the theorem prover. The paper discusses ways of resolving this as well as other problems identified in this study.
机译:本文描述了一种在情境意识背景下关系推导的案例研究。首先,我们介绍了一个由模拟的1级系统提供输入的场景。输入是用来自本体的术语注释的事件,用于情境感知。该本体包含用于表示情况和对情况进行推理的概念。事件的本体和注释以DAML和Rule-ML表示,然后系统地转换为称为MetaSlang的形式化方法语言。用形式化方法语言表示所有信息后,我们可以使用定理证明者SNARK来证明1级对象之间的给定关系成立(或不成立)。本文显示了一种概念证明,即可以在一个正式框架内完成情境意识中的关系推导。它还确定了与此方法相关的瓶颈,例如定理证明者可能必须考虑的大量潜在关系的问题。本文讨论了解决此问题的方法以及本研究中发现的其他问题。

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