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Ontology Based Context Model

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Ontology Based Context Model. Yingyi Bu NjuIcs. 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.

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Ontology Based Context Model

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  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!

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