@Inproceedings{Bic11a,
   joint-pub = {false},
   status = {public},
   task = {T3.1, T3.3},
   publisher = {IEEE CS Press},
   month = {September},
   booktitle = {1st DEXA Workshop on Information Systems for Situation Awareness and Situation Management},
   address = {Toulouse (F)},
   reported = {year1},
   year = {2011},
   invited = {no},
   timestamp = {2012.10.31},
   main = {no},
   accessible = {true},
   title = {{Improving Situation Recognition via Commonsense Sensor Fusion}},
   author = {Nicola Bicocchi and Gabriella Castelli and Marco Mamei and Franco Zambonelli},
   period = {year1},
   abstract = {Pervasive services often rely on multi-modal clas- sification to implement situation-recognition capabilities. How- ever, 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 ConceptNet 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.},
   owner = {kroiss},
   ascens_ref = {true},
   partner = {UNIMORE},
   wp = {WP3}
}