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Uncertain Context Modeling of Dimensional Ontology Using Fuzzy Subset Theory

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Scalable Uncertainty Management (SUM 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5291))

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Abstract

Context-sensitive knowledge is widespread in Semantic Web, but traditional RDF triples lack references to situations, points in time, or generally contexts. In order to resolve this problem, Dimensional Ontology (DO) theory is put forward, which features dimensional relations, dimensional operators as well as reasoning mechanism for context-sensitive knowledge. The notion of context in DO is actuarially a vector of dimensions, which are crisp sets representing certain contextual aspects. We propose an approach of modeling uncertain contexts of DO through fuzzy subsets instead of crisp ones. In this way, DO provides the ability of representing fuzzy triples in uncertain contexts. Apart from describing uncertain context model of DO, we discuss how dimensional operators and reasoning mechanism can be applied to uncertain contexts to allow more complex manipulations.

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Jiang, Y., Dong, H. (2008). Uncertain Context Modeling of Dimensional Ontology Using Fuzzy Subset Theory. In: Greco, S., Lukasiewicz, T. (eds) Scalable Uncertainty Management. SUM 2008. Lecture Notes in Computer Science(), vol 5291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87993-0_21

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  • DOI: https://doi.org/10.1007/978-3-540-87993-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87992-3

  • Online ISBN: 978-3-540-87993-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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