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• W76248504 abstract "A major challenge of pervasive context-aware computing and intelligent environments resides in the acquisition and modelling of rich and heterogeneous context data. Decisive aspects of this information are the ongoing human activities at different degrees of granularity.We conjecture that ontology-based activity models are key to support interoperable multilevel activity representation and recognition. In this paper, we report onan initial investigation about the application of probabilistic description logics (DLs) to a framework for therecognition of multilevel activities in intelligent environments. In particular, being based on Log-linear DLs, our approach leverages the potential of highly expressive description logics with probabilistic reasoning in one unified framework. While we believe that this approach is very promising, our preliminary investigation suggests that challenging research issues remain open, including extensive support for temporal reasoning, and optimizations to reduce the computational cost." @default.
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• W76248504 date "2012-07-15" @default.
• W76248504 modified "2023-10-18" @default.
• W76248504 title "Towards Activity Recognition Using Probabilistic Description Logics" @default.
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