Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i
) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii
) outperforms the original MLNs-based method in dealing with dynamic data.
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