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Conceptual Situation Spaces for Semantic Situation-Driven Processes

机译:语义情况驱动过程的概念情况空间

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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:上下文意识是跨多个应用程序域中的高度期望的功能。语义Web服务(SWS)技术通过基于综合语义表示来实现对给定任务的分布式Web服务的自动发现来解决上下文适应。虽然SWS技术支持基于语义的资源分配,但它不会为特定情况发现适当的SWS表示。通过符号SWS表示设施描述所有方面的局面的复杂概念是一个昂贵的任务,这可能永远不会导致语义完整性并引入歧义问题。此外,尽管没有任何现实世界的情况完全等于另一个,但它必须与SWS表示中的有限的参数描述匹配,以实现上下文适应性。为了克服这些问题,我们建议概念情况空间(CSS),以便在概念空间思想之后的几何矢量空间中的成员描述情况特征的描述。 CSS使基于模糊的相似性匹配在现实情况特征和预定义的情况描板之间。在我们的愿景之后,后者是语义情况驱动过程(SDP)描述的一部分,其定义了适合支持演变情况的过程的SWS目标的组成。特别是,我们指的是SWS的WSMO方法。因此,我们的方法通过为特定情况实现自动发现,构成和执行可实现的目标来扩展WSMO的表现力。为了证明可行性,我们将我们的方法应用于电子学习领域,并提供概念验证原型。

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