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Improving Situation Recognition via Commonsense Sensor Fusion

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4 Author(s)
Bicocchi, N. ; Dipt. di Ing. deli''Inf., Univ. di Modena e Reggio Emilia, Modena, Italy ; Castelli, G. ; Mamei, M. ; Zambonelli, F.

Pervasive services often rely on multi-modal classification to implement situation-recognition capabilities. However, current classifiers are still inaccurate and unreliable. In this paper we present preliminary results obtained with a novel approach that combines well established classifiers using a commonsense knowledge base. The approach maps classification labels produced by independent classifiers to concepts organized within the Concept Net network. Then it verifies their semantic proximity by implementing a greedy approximate sub-graph search algorithm. Specifically, different classifiers are fused together on a commonsense basis for both: (i) improve classification accuracy and (ii) deal with missing labels. Experimental results are discussed through a real-world case study in which two classifiers are fused to recognize both user's activities and visited locations.

Published in:

Database and Expert Systems Applications (DEXA), 2011 22nd International Workshop on

Date of Conference:

Aug. 29 2011-Sept. 2 2011

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