Conceptual situation spaces for semantic situation-driven processes

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Abstract

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. © 2008 Springer-Verlag Berlin Heidelberg.

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APA

Dietze, S., Gugliotta, A., & Domingue, J. (2008). Conceptual situation spaces for semantic situation-driven processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5021 LNCS, pp. 599–613). https://doi.org/10.1007/978-3-540-68234-9_44

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