Towards the Semantic Web: Ontology-driven Knowledge Management
Towards the Semantic Web: Ontology-driven Knowledge Management
Intelligent Agents Meet the Semantic Web in Smart Spaces
IEEE Internet Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Dynamic context management for pervasive applications
The Knowledge Engineering Review
Towards an Affective Aware Home
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
A user meta-model for context-aware recommender systems
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
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In ubiquitous computing, behavior routine learning is the process of mining the context-aware data to find interesting rules on the user’s behavior, while preference learning tries to utilize the user’s behavior information to infer user interests, intention and desires. An intelligent environment should be adaptive, i.e. it is should be able to learn the routine and preference of user, then provide user with the suitable service. Developing intelligent ubiquitous environment requires not only good learning algorithms but also appropriate reusable models of user preference and behavior routine, which are not fully covered by current projects. In this paper, we propose a formal and comprehensive ontology-based model of user preference and behavior routine. The implementation of the ontology using OWL[14] enhances the expressiveness, support inference, knowledge reuse and knowledge sharing, which we can not achieve by normal models. The main benefit of this model is the ability to reason over context data to predict what the user wants the system to do. Based on our model, we also present a rule learning mechanism to learn the preference and behavior rules from context data.