Dietze, Stefan; Gugliotta, Alessio and Domingue, John
(2009). Bridging the gap between mobile application contexts and Semantic Web resources.
In: Stojanovic, Dragan ed.
Context-Aware Mobile and Ubiquitous Computing for Enhanced Usability: Adaptive Technologies and Applications.
Premier Reference Source.
Information Science Reference, pp. 217–234.
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Context-awareness is highly desired, particularly in highly dynamic mobile environments. Semantic Web Services (SWS) address context-adaptation by enabling the automatic discovery of distributed Web services based on comprehensive semantic capability descriptions. Even though the appropriateness of resources in mobile settings is strongly dependent on the current situation, SWS technology does not explicitly encourage the representation of situational contexts. Therefore, whereas SWS technology supports the allocation of resources, it does not entail the discovery of appropriate SWS representations for a given situational context. Moreover, describing the complex notion of a specific situation by utilizing symbolic SWS representation facilities is costly, prone to ambiguity issues and may never reach semantic completeness. In fact, since not any real-world situation completely equals another, a potentially infinite set of situation parameters has to be matched to a finite set of semantically defined SWS reso rce descriptions to enable context-adaptability. To overcome these issues, the authors propose Mobile Situation Spaces (MSS) which enable the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces (CS). Semantic similarity between situational contexts is calculated in terms of their Euclidean distance within a MSS. Extending merely symbolic SWS descriptions with context information on a conceptual level through MSS enables similarity-based matchmaking between real-world situation characteristics and predefined resource representations as part of SWS descriptions. To prove the feasibility, the authors provide a proof-of-concept prototype which applies MSS to support context-adaptation across distinct mobile situations.
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