@Inproceedings{Bic11b,
   joint-pub = {false},
   status = {public},
   task = {T3.1, T3.3},
   publisher = {Springer Verlag},
   month = {November},
   booktitle = {International Joint Conference on Ambient Intelligence},
   address = {Amsterdam (NL)},
   reported = {year1},
   series = {Lecture Notes in Computer Science},
   year = {2011},
   invited = {no},
   timestamp = {2012.10.31},
   main = {no},
   accessible = {true},
   title = {{Augmenting Mobile Localization with Activities and Common Sense Knowledge}},
   author = {Nicola Bicocchi and Gabriella Castelli and Marco Mamei and Franco Zambonelli},
   period = {year1},
   abstract = {Location is a key element for ambient intelligence services. Due to GPS inaccuracies, inferring high level information (i.e., being at home, at work, in a restaurant) from geographic coordinates in still non trivial. In this paper we use information about activities being performed by the user to improve loca- tion recognition accuracy. Unlike traditional methods, relations between locations and activities are not extracted from training data but from an external common- sense knowledge base. Our approach maps location and activity labels to con- cepts organized within the ConceptNet network. Then, it verifies their common- sense proximity by implementing a bio-inspired greedy algorithm. Experimental results show a sharp increase in localization accuracy.},
   owner = {kroiss},
   ascens_ref = {true},
   partner = {UNIMORE},
   wp = {WP3}
}