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Conceptual Situation Spaces for Semantic Situation-Driven Processes

机译:语义情境驱动过程的概念情境空间

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Context-awareness is a highly desired feature across several application domains. Semantic Web Services (SWS) technologies address context-adaptation by enabling the automatic discovery of distributed Web services for a given task based on comprehensive semantic representations. Whereas SWS technology supports the allocation of resources based on semantics, it does not entail the discovery of appropriate SWS representations for a given situation. Describing the complex notion of a situation in all its facets through symbolic SWS representation facilities is a costly task which may never lead to semantic completeness and introduces ambiguity issues. Moreover, even though not any real-world situation completely equals another, it has to be matched to a finite set of parameter descriptions within SWS representations to enable context-adaptability. To overcome these issues, we propose Conceptual Situation Spaces (CSS) to facilitate the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. CSS enable fuzzy similarity-based matchmaking between real-world situation characteristics and predefined situation descriptions. Following our vision, the latter are part of semantic Situation-Driven Process (SDP) descriptions, which define a composition of SWS Goals suitable to support the course of an evolving situation. Particularly, we refer to the WSMO approach for SWS. Consequently, our approach extends the expressiveness of WSMO by enabling the automatic discovery, composition and execution of achievable goals for a given situation. To prove the feasibility, we apply our approach to the domain of eLearning and provide a proof-of-concept prototype.
机译:跨上下文意识是跨多个应用程序领域的高度期望的功能。语义Web服务(SWS)技术通过基于综合语义表示针对给定任务启用分布式Web服务的自动发现,从而解决了上下文适应问题。尽管SWS技术支持基于语义的资源分配,但它并不意味着需要为给定情况找到合适的SWS表示形式。通过符号SWS表示工具在各个方面描述情境的复杂概念是一项代价高昂的工作,它可能永远不会导致语义完整性,并且会引入歧义性问题。而且,即使现实世界中没有任何情况完全不同,它也必须与SWS表示中的一组有限的参数描述相匹配才能实现上下文适应性。为了克服这些问题,我们提出概念性情境空间(C​​SS),以遵循概念性空间的思想,方便地将作为几何矢量空间成员的情境特征描述为概念。 CSS使现实情况特征与预定义的情况描述之间基于模糊相似度的匹配成为可能。遵循我们的愿景,后者是语义情境驱动过程(SDP)描述的一部分,该情境定义了适用于支持不断变化的情境的SWS目标的组成。特别是,我们指的是用于SWS的WSMO方法。因此,我们的方法通过针对给定情况实现自动发现,组合和执行可实现的目标,扩展了WSMO的表达能力。为了证明可行性,我们将我们的方法应用于电子学习领域,并提供了概念验证原型。

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