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Active Perception over Machine and Citizen Sensing

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Presentation given at the Semantic Technology Conference 2011 (http://semtech2011.semanticweb.com/)

Presentation given at the Semantic Technology Conference 2011 (http://semtech2011.semanticweb.com/)


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  • Cory Henson (delivered 07/07/10)
  • Transcript

    • 1. 1
    • 2. A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data
      - GigaOmni Media
      2
    • 3. In the next few years, sensors networks will produce
      10-20 times the amount of generated by social media
      - GigaOmni Media
      3
    • 4. Active Perception
      over Machine and Citizen Sensing
      Cory Henson and AmitSheth
      Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
      Wright State University, Dayton, Ohio, USA
      4
    • 5. To enable situation awareness on the Web, we must utilize abstractions capable of representing observations and perceptions generated by either people or machines.
      Web
      observe
      perceive
      “real-world”
      5
    • 6. For example, both people and machines are capable of observing qualities, such as redness.
      observes
      Observer
      Quality
      * Formally described in a sensor/observation ontology
      6
    • 7. Sensor and Sensor Network (SSN) Ontology
      http://www.w3.org/2005/Incubator/ssn/wiki/
      7
    • 8. The ability to perceive is afforded through the use of background knowledge, relating observable qualities to entities in the world.
      Quality
      * Formally described in domain ontologies
      (and knowledge bases)
      inheres in
      Entity
      8
    • 9. http://linkedsensordata.com
      9
    • 10. With the help of sophisticated inference, both people and machines are also capable of perceiving entities, such as apples.
      perceives
      Entity
      Perceiver
      • the ability to degrade gracefully with incomplete information
      • 11. the ability to minimize explanations based on new information
      • 12. the ability to reason over data on the Web
      • 13. fast (tractable)
      10
    • 14. minimize
      explanations
      tractable
      degrade gracefully
      Web reasoning
      Web Ontology
      Language (OWL)
      Parsimonious Covering Theory (PCT)
      11
    • 15. Conversion of PCT to OWL 2 (EL)
      Parsimonious
      Covering Theory
      (Abductive Logic)
      *
      OWL-DL
      • Cory Henson, KrishnaprasadThirunarayan, AmitSheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. In: Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, United States, June 5-6, 2011.
      12
      12
    • 16. The ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers.
      Observer
      sends
      observation
      sends
      focus
      Traditionally called the Perception Cycle
      (or Active Perception)
      Perceiver
      13
    • 17. Nessier’s Perception Cycle
      14
    • 18. Cognitive Theory of Perception (timeline)
      • 1970’s - Perception is an active, cyclical process of exploration and interpretation
      - Nessier’s Perception Cycle
      • 1980’s - The perception cycle is driven by background knowledge in order to generate and test hypotheses.
      - Richard Gregory (optical illusions)
      • 1990’s - In order to effectively test hypotheses, some observations are more informative than others.
      - Norwich’s Entropy Theory of Perception
      15
    • 19. Integrated together, we have an general model – capable of abstraction – relating observers, perceivers, and background knowledge.
      observes
      Observer
      Quality
      sends
      observation
      sends
      focus
      inheres in
      perceives
      Entity
      Perceiver
      16
    • 20. i
      ntelleg
      “to perceive”
      17
    • 21. Application of
      Weather
      Traffic
      18
      18
    • 22. Traffic Application
      19
    • 23. Detection of events, such as blizzards, from weather station observations on LinkedSensorData
      Weather Application
      50% savings in resource requirements needed for detection
      20
    • 24. thank you, and please visit us at
      http://semantic-sensor-web.com
      Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
      Wright State University, Dayton, Ohio, USA
      21