Talking with Computers about the Future We Shall Share
COMPSAC '99 23rd International Computer Software and Applications Conference
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
How to wreck a nice beach you sing calm incense
Proceedings of the 10th international conference on Intelligent user interfaces
Human dynamics: computation for organizations
Pattern Recognition Letters - Special issue: Advances in pattern recognition
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Supporting location-aware services for mobile users with the whereabouts diary
Proceedings of the 1st international conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Common sense reasoning – from cyc to intelligent assistant
Ambient Intelligence in Everyday Life
Augmenting mobile localization with activities and common sense knowledge
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
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Recent mobile computing applications try to automatically identify the places visited by the user from a log of GPS readings. Such applications reverse geocode the GPS data to discover the actual places shops, restaurants, etc. where the user has been. Unfortunately, because of GPS errors, the actual addresses and businesses being visited cannot be extracted unambiguously and often only a list of candidate places can be obtained. Commonsense reasoning can notably help the disambiguation process by invalidating some unlikely findings e.g., a user visiting a cinema in the morning. This paper illustrates the use of Cyc-an artificial intelligence system comprising a database of commonsense knowledge-to improve automatic place identification. Cyc allows to probabilistically rank the list of candidate places in consideration of the commonsense likelihood of that place being actually visited on the basis of the user profile, the time of the day, what happened before, and so forth. The system has been evaluated using real data collected from a mobile computing application.