Personis: A Server for User Models
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
A Hybrid Location Model with a Computable Location Identifier for Ubiquitous Computing
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Location Aggregation from Multiple Sources
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Use of ontologies in a pervasive computing environment
The Knowledge Engineering Review
Intelligent Agents Meet the Semantic Web in Smart Spaces
IEEE Internet Computing
ONCOR: ontology- and evidence-based context reasoner
Proceedings of the 12th international conference on Intelligent user interfaces
Context obfuscation for privacy via ontological descriptions
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
A Framework for Creating and Using Maps of Privately Owned Spaces
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Exploiting acoustic source localization for context classification in smart environments
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
On using temporal features to create more accurate human-activity classifiers
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Expert Systems with Applications: An International Journal
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Location modelling is central for many pervasive applications and is a key challenge in this area. One major difficulty in location modelling is due to the nature of evidence about a person's location; the evidence is commonly noisy, uncertain and conflicting. Ontological reasoning is intuitively appealing to help address this problem, as reflected in several previous proposals for its use.This paper makes several important contributions to the exploration of the potential power of ontologies for improving reasoning about people's location from the available evidence. We describe ONCOR, our lightweight ontology framework: it has the notable and important property that it can be semi-automatically constructed, making new uses of it practical. This paper provides a comprehensive evaluation on how ontological reasoning can support location modelling: we introduce three algorithms for such reasoning and their evaluation based on a study of 8 people over 10---13 days. The results indicate the power of the approach, with mean error rates dropping from 55% with a naive algorithm to 16% with the best of the ontologically based algorithms. This work provides the first implementation of such an approach with a range of ontological reasoning approaches explored and evaluated.