Agents that reduce work and information overload
Communications of the ACM
Understanding and Using Context
Personal and Ubiquitous Computing
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
Designing mediation for context-aware applications
ACM Transactions on Computer-Human Interaction (TOCHI)
Usage patterns of collaborative tagging systems
Journal of Information Science
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Network properties of folksonomies
AI Communications - Network Analysis in Natural Sciences and Engineering
Folksonomy-Based Indexing for Location-Aware Retrieval of Learning Contents
WMUTE '08 Proceedings of the Fifth IEEE International Conference on Wireless, Mobile, and Ubiquitous Technology in Education
Towards a Tag-Based User Model: How Can User Model Benefit from Tags?
UM '07 Proceedings of the 11th international conference on User Modeling
Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Proceedings of the 2nd International Workshop on Location and the Web
Adaptive systems in the era of the semantic and social web, a survey
User Modeling and User-Adapted Interaction
An operational definition of context
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Location-based context retrieval and filtering
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
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Most context-aware systems rely on physical sensors. To some extent, these systems are able to reason about a user's situation by means of the measured data. However, their overall uncertainty in modeling human behavior leads to ambiguity. A language for the mediation of context information between the user and the system is required to enable the user to adjust the machine's interpretation of his context. We describe how keywords that are attributed to activities by users themselves can act as such a mediator. We present results of a study that investigates the nature of this context attributes. The results demonstrate that different users use similar keywords to describe similar situations and different keywords to describe different situations. Therefore, algorithms developed to evaluate the semantic relatedness of tags and resources within folk-sonomies can be applied to exploit knowledge about the users' contexts from the keywords they have assigned. We discuss a prototype that recommends mobile services by enhancing its model with the described keywords.