L. Obrst, S. Stoutenburg, D. McCandless, D. Nichols, Paul Franklin, Michael Prausa, Richard Sward
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引用次数: 11
摘要
本体支持集体概念的显式表达,并支持语义级别的机器对机器(M2M)交互。用标准语言(如Web Ontology language (OWL))表示并在网络上公开的本体,提供了前所未有的互操作性解决方案的潜力,因为它们具有丰富的语义、计算机可解释性和固有的可扩展性。在本章中,我们描述了我们如何在OWL中应用本体来实现异构数据源的快速企业集成,以跟踪战场空间中的对象。我们发现,一旦建立了健壮的基础领域本体,就可以轻松快速地集成新数据源,从而快速提供新的系统功能。特别地,我们展示了移动轨迹如何与情报和空间事件快速集成,以使用本体提供增强的态势感知。本章还描述了我们开发的整体SEER和SWORIER系统,后者将OWL本体(和RDF实例)和语义Web规则语言(SWRL)规则转换为Prolog,应用知识编译技术,然后在运行时利用OWL/逻辑编程推理进行高效的自动推理。我们还简要描述了最近对原型和本体的扩展,以解决无人驾驶自动车辆(UAV)规避的更严格的地理空间规则。最后,我们考虑了我们的工作和未来的研究方向提出的一些问题,以解决这些问题。
Ontologies for Rapid Integration of Heterogeneous Data for Command, Control, & Intelligence
Ontologies enable explicit expression of collective concepts and support Machine-to-Machine (M2M) interactions at the semantic level. Ontologies expressed in a standard language, such as the Web Ontology Language (OWL) and exposed on a network offer the potential for unprecedented interoperability solutions since they are semantically rich, computer interpretable and inherently extensible. In this chapter, we describe how we applied ontologies in OWL for rapid enterprise integration of heterogeneous data sources to track objects in a battlespace. We found that once a robust foundational domain ontology is established, it is easy and quick to integrate new data sources and therefore rapidly provide new system capabilities. In particular, we demonstrate how moving tracks can be quickly integrated with intelligence and space events to provide enhanced situational awareness using ontologies. This chapter also describes the overall SEER and SWORIER systems we developed, the latter of which translated OWL ontologies (and RDF instances) and Semantic Web Rule Language (SWRL) rules into Prolog, applied knowledge compilation techniques, and then at runtime, utilized a combined OWL/logic programming reasoned for efficient automated reasoning. We also briefly describe a more recent extension to the prototype and to the ontologies that we made to address more rigorous geospatial rules for unmanned autonomous vehicle (UAV) avoidance. Finally, we consider some issues raised by our work and future lines of research to address these.