1. Ontology Based Context Model Yingyi Bu NjuIcs

2. What is Context? Any information that can be used characterize the situation of an entity, where an entity can be a person, place, or physical or computational object Context is computer’s view of real world situations.

3. Research Intentions Mapping Contexts in Real World to Contexts in Computers Exactly or Approximately Discovering Implicit Contexts Sharing Contexts among Heterogeneous Software Entities

4. Chanllenges Uniform Context Representation Context Fusion Context Caching and Update Scheme Activity Recognition Goal Recognition Adaptive Behaviors ……

5. Context Modeling Approach Key-Value XML Object ER-UML Ontology Graph

6. Ontology Based Context Model Why?  Unifying context representation, 5-tuples or 8-tuples  Reasoning high-level implicit contexts  Facilitating Conflict Detection  Easy for sharing  Unifying Query Mechanism

7. Ontology Based Context Model What?  Ontology  Persistent Contexts  Dynamic Contexts  Ontology Reasoning and Rule Reasoning

8. Ontology Based Context Model Case  Byy locateIn Room506 － > Byy locateIn MMW  Byy talkTo Txp, Txp talkTo Xxm － > Byy talkTo Xxm  Byy talkTo Txp － > Txp type Person  Byy locateIn Room506, Byy talkTo Txp, Byy near Desk － > Byy giveLecture Room506

9. Ontology Based Context Model Implementations:  Ontology construction: OWL on Protégé.  Reasoning and conflict detection: Jena2.2

10. Ontology Based Context Model Roles  Raw Context Provider  Context Service  Context Consumer

11. Ontology Based Context Model Query  RDQL language: Select ?x where (?x giveLecture ?y), (?x type Student), (?x locateIn MMW)

12. Benifits Fusing Context Formally Detecting Conflict Easily Querying contexts easily

13. Drawbacks Time-consuming Dynamic context management

14. Future Work ER Graph-based Context Model Sensor-based Activity Recognition  Segmentation of Sensed Data  Semi-supervised learning Anomaly Detection Algorithms for Healthcare Learning-based Adaptive Behaviors

15. Reference Jie Yin, Dou Shen, Qiang Yang and Ze-nian Li Activity Recognition through Goal-Based Segmentation. Proceedings of the Twentieth National Conference on Artificial Intelligence, AAAI 2005, Pittsburg, PA USA, July 2005. Pages 28--33. T. Strang, C. Linnhoff-Popien. A Context Modeling Survey.Workshop on Advanced Context Modelling, Reasoning and Management as part of UbiComp 2004 - The Sixth International Conference on Ubiquitous Computing, Nottingham/England, September 2004. T. Gu, H. K. Pung, D. Q. Zhang. Towards an OSGi-Based Infrastructure for Context-Aware Applications in Smart Homes, IEEE Pervasive Computing, 3(4) 2004, 66-74. H. Chen, T. W. Finin, A. Joshi, L. Kagal, F. Perich, D. Chakraborty. Intelligent Agents Meet the Semantic Web in Smart Spaces. IEEE Internet Computing, (November 2004):69-79.

16. Thanks!