1<br />
A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data<br />-...
In the next few years, sensors networks will produce<br />10-20 times the amount of generated by social media<br />- GigaO...
Active Perception <br />over Machine and Citizen Sensing<br />Cory Henson and AmitSheth<br />Kno.e.sis – Ohio Center of Ex...
To enable situation awareness on the Web, we must utilize abstractions capable of representing observations and perception...
For example, both people and machines are capable of observing qualities, such as redness.<br />observes<br />Observer<br ...
Sensor and Sensor Network (SSN) Ontology<br />http://www.w3.org/2005/Incubator/ssn/wiki/<br />7<br />
The ability to perceive is afforded through the use of background knowledge, relating observable qualities to entities in ...
http://linkedsensordata.com<br />9<br />
With the help of sophisticated inference, both people and machines are also capable of perceiving entities, such as apples...
 the ability to minimize explanations based on new information
 the ability to reason over data on the Web
 fast (tractable)</li></ul>10<br />
minimize<br />explanations<br />tractable<br />degrade gracefully<br />Web reasoning<br />Web Ontology<br />Language (OWL)...
Conversion of PCT to OWL 2 (EL)<br />Parsimonious<br />Covering Theory<br />(Abductive Logic)<br />*<br />OWL-DL<br /><ul>...
The ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceiv...
Nessier’s Perception Cycle<br />14<br />
Cognitive Theory of Perception (timeline)<br /><ul><li>1970’s - Perception is an active, cyclical process of exploration a...
Integrated together, we have an general model – capable of abstraction – relating observers, perceivers, and background kn...
i<br />ntelleg<br />“to perceive”<br />17<br />
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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/)

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  • Cory Henson (delivered 07/07/10)
  • Active Perception over Machine and Citizen Sensing

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

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