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Fuzzy context adaptation through conceptual situation spaces

Dietze, Stefan; Gugliotta, Alessio and Domingue, John (2008). Fuzzy context adaptation through conceptual situation spaces. In: 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) within 2008 IEEE World Congress on Computational Intelligence (IEEE WCCI2008), 1-6 Jun 2008, Hong Kong.

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Context-adaptive information systems (IS) are highly desired across several application domains and usually rely on matching a particular real-world situation to a finite set of predefined situation parameters. To represent context parameters, semantic and non-semantic representation standards are widely used. However, describing the complex and diverse notion of specific situations is costly and may never reach semantic completeness. Whereas not any situation parameter completely equals another, the number of (predefined) representations of situation parameters is finite. Moreover, following symbolic representation approaches leads to ambiguity issues and does not entail semantic meaningfulness. Consequently, the challenge is to enable fuzzy matchmaking methodologies to match real-world situation characteristics to a finite set of predefined situation descriptions. In this paper, we propose conceptual situation spaces (CSS) which enable the description of situation characteristics as members in geometrical vector spaces following the idea of conceptual spaces. Consequently, fuzzy matchmaking is supported by calculating the semantic similarity between the current situation and prototypical situation descriptions in terms of their Euclidean distance within a CSS. Aligning CSS to existing symbolic representation standards, enables the automatic matchmaking between real-world situation characteristics and symbolic parameter representations. To prove the feasibility, we apply our approach to the domain of e-learning.

Item Type: Conference or Workshop Item
Copyright Holders: 2008 IEEE
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23035
Depositing User: Kay Dave
Date Deposited: 20 Sep 2010 13:48
Last Modified: 15 Jan 2019 06:08
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