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Ontology Based Context Model

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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