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Semantics empowered
   Physical-Cyber-Social Systems for
              EarthCube
Presentation at theEarthCubeFace Face-to-Face Workshop of Semantics & Ontologies
                   Workgroup: April 30-May 1, 2012, Ballston, VA.

                                Amit Sheth
  Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
                Wright State University, Dayton, OH, USA
                             http://knoesis.org



       Special thanks & contributions: Cory Henson, PramodAnantharam
                                                                                   1
Web (and associated computing) is evolving
                                          Computing for Human Experience
Enhanced Experience,
Tech assimilated in life                   Web as an oracle / assistant / partner
                                             - “ask the Web”: using semantics to leverage
Situations,           2007                 text + data + services
Events                                       - Powerset, Siri, Watson         Web 3.0
Objects                                Web ofpeople, Sensor Web
                                         - social networks, user-createdcasualcontent
                                       - 40 billionsensors
Patterns                                                                     Web 2.0
                             Web of resources
                              - data, service, data, mashups
Keywords                      - 4 billionmobilecomputing
            Web of databases
1997          - dynamically generated pages
              - web query interfaces
   Web of pages
     - text, manually created links                             Web 1.0
     - extensive navigation
Sensors everywhere ..sensing, computing,
transmitting
• 2009: 1.1 billion PCs,
  4 billion mobile devices,
  40+ billion mobile sensors
  (Nokia: Sensing the World with Mobile Devices)

• 6 billion intelligent sensors
   – informed observers, rich local knowledge



                            Christmas Bird Count




                                                   3
Data & Knowledge Ecosystem


                                  Situational Awareness

         Decision Support

                                                    Insight             Knowledge Discovery
                       Analysis (eg Patterns)
    Understanding & Perception                                                Data Mining
SSW/
W3C-SSN           Search             Browsing                 Integration


OGC SWE
                                                 Multimedia Data
                                                                             Structured,
 Textual Data: Scientific Literature, Web Pages, News, Blogs,
                                                                             Semistructured
              Reports, Wiki, Forums, Comments, Tweets
                                                                             Unstructured
                           Observational Data      Experimental Data         Data
  Transactional Data

                                                                                            4
Semantics as core enabler, enhancer @ Kno.e.sis

                                                             15 faculty
                                                   ~50 PhD students
                                     Excellent Industry collaborations
                                    (MSFT, GOOG, IBM, Yahoo!, HP)
                                                          Well funded
                                               Exceptional Graduates
                                                     Multidisciplinary:
                                                     Health/Clinical
                                                     Biomedical Sc
                                                           Social Sc
                                                                   …




                                      5
Semantic
Models                                                                  Search
                                                                        Integration
                                                                        Analysis
                                                                        Discovery
                                                Relationship Web        Question
                                                                         Answering
                          Patterns / Inference / Reasoning
                                                                        Situational
                                                    Meta data /           Awareness
                                                    Semantic
                                                    Annotations
                               Metadata Extraction


                    RDB




                                     Text
           Structured and Semi-                   Multimedia Content   Sensor Data
              structured Data                       and Web Data
From simple ontologies




Knowledge Enabled Information and Services Science
Drug Ontology Hierarchy
(showing is-a relationships)



                                                  formulary_
           non_drug_            interaction_       property                formulary
            reactant              property
                                                                                                 indication
                       indication_                           property
                                                                                  owl:thing
    monograph            property
     _ix_class                          prescription                                             interaction_
                                          _drug_                                                  with_non_
                   brandname_                               prescription
                                        brand_name                                              drug_reactant
   prescription     individual                                 _drug            interaction
     _drug_
    property                      brandname_
                  brandname_       composite       prescription                               interaction_
                   undeclared                        _drug_                                   with_mono
                                                                            interaction_
                                                     generic                                  graph_ix_cl
                                                                            with_prescri
       cpnum_                      generic_                                                        ass
                                                                             ption_drug
        group                     composite
                                                        generic_
                                                       individual


                           Knowledge Enabled Information and Services Science
to complex ontologies




Knowledge Enabled Information and Services Science
N-Glycosylation metabolic pathway


                                                      GNT-I
                                         attaches GlcNAc at position 2
    N-glycan_beta_GlcNAc_9                  N-acetyl-glucosaminyl_transferase_V
                                                              N-glycan_alpha_man_4




                     GNT-V
       attaches GlcNAc at position 6
        UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2
                                             <=>
   UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2



       UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021

                       Knowledge Enabled Information and Services Science
A little bit about semantic metadata
     extractions and annotations




     Knowledge Enabled Information and Services Science
Extractionfor Metadata Creation


                                              Nexis       Digital Videos
                                               UPI
                                               AP
                                                        ...                  ...
                                              Feeds/                               Data Stores
                                            Documents
                      WWW, Enterprise                         Digital Maps
                       Repositories
                                                                   ...
                                               Digital Images            Digital Audios




    Create/extract as much (semantics)
    metadata automatically as possible;
   Use ontlogies to improve and enhance             EXTRACTORS
                 extraction

                                                        METADATA


                  Knowledge Enabled Information and Services Science
Automatic Semantic Metadata
Extraction/Annotation of Textual Data




             Knowledge Enabled Information and Services Science
Semantic Sensor Web Infrastructure
Semantically Annotated O&M




<om:Observation>
<om:samplingTime><gml:TimeInstant>...</gml:TimeInstant>
</om:samplingTime>
<om:procedurexlink:role="http//www.w3.org/2009/Incubator/ssn/ontologies/SensorOntolgy.owl#Sensor“
xlink:href="http//www.w3.org/2009/Incubator/ssn/ontologies/SensorOntolgy.owl#sensor_xyz"/>
<om:observedPropertyxlink:href="http//www.w3.org/2009/Incubator/ssn/ontologies/SensorOntolgy.owl#temperature"/>
<featureOfInterestxlink:href="http://sws.geonames.org/5758442/"/>
<om:resultuom="http//www.w3.org/2009/Incubator/ssn/ontologies/SensorOntolgy.owl#fahrenheit">42.0</om:result>
</om:Observation>



                                                                                                                  15
Semantic Sensor ML – Adding Ontological Metadata

  Domain                        Person
  Ontology
                      Company




    Spatial
   Ontology                   Coordinates


                  Coordinate System




  Temporal
  Ontology
                           Time Units

               Timezone




 Mike Botts, "SensorML and Sensor Web Enablement,"   16
     Earth System Science Center, UAB Huntsville
Workflow Architecture for Managing Streaming Sensor Data
Weather Application
Weather Application




                   Detection of events, such as blizzards, from weather
                   station observations on LinkedSensorData



                                                                          18
               Demos: Real-Time Feature Streams
SECURE: Semantics Empowered Rescue Application
                          Weather Environment




Rescue robots detect different types of fires, which may require different
methods/tools to extinguish, and relays this knowledge to first responders.



                      Demo: SECURE: Semantics Empowered Rescue Environment    19
A Challenging Example Query


What schools in Ohio should now be closed due to inclement
weather?
Need domain ontologies and rules to describe type of inclement
weather and severity.

Integrationof technologies needed to answer query
       1. Spatial Aggregation
       2. Semantic Sensor Web
       3. Machine Perception
       4. Linked Sensor Data
       5. Analysis of Streaming Real-Time Data
 More details in: Spatial Semantics for Better Interoperability and Analysis: Challenges and Experiences in
 Building Semantically Rich Applications in Web 3.0



20
Technology 1
     Spatial Aggregation



 • What schools are in Ohio?
 • What weather sensors are near each of the
 school?



21
Technology 2
     Semantic Sensor Web (SSW)
 • What is inclement weather?
 • What sensors in Ohio are capable of detecting inclement
 weather?
 • What sensors are near schools in Ohio?
 • What observations are these sensors generating NOW?




22
Technology 3
         Active Machine Perception
     • Are these observations providing evidence for
       inclement weather?




23
Technology 4
            Linked Sensor Data
 • What schools are in Ohio?
 • What inclement weather necessitates school closings?
 • What sensors in Ohio are capable of detecting inclement
 weather?
 • What sensors are near schools in Ohio?
 • What observations are these sensors generating NOW?


24
Technology 5
 Analysis of Streaming Real-Time
               Data

     • What observations are these sensors
     generating
       NOW?

25
Semantics empowered Physical-Cyber-Social Systems for EarthCube
Demos

•   Real-Time Feature Streams
•   SECURE(presentation:
•   SECURE: Semantics Empowered resCUe EnviRonmEnt )Amit
•   Trusted Perception Cycle
•   Sensor Discovery on Linked Data
•   Semantic Sensor Observation Service (SemSOS)

Related Talk
• Spatial Semantics for Better Interoperability and Analysis:
  Challenges and Experiences in Building Semantically Rich
  Applications in Web 3.0: Amit Sheth delivers talk at the 3rd Annual
  Spatial Ontology Community of Practice Workshop:
  Development, Implementation and Use of Geo-Spatial Ontologies
  and Semantics, 3 October 2010, USGS, Reston, VA.

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Semantics empowered Physical-Cyber-Social Systems for EarthCube

  • 1. Semantics empowered Physical-Cyber-Social Systems for EarthCube Presentation at theEarthCubeFace Face-to-Face Workshop of Semantics & Ontologies Workgroup: April 30-May 1, 2012, Ballston, VA. Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, OH, USA http://knoesis.org Special thanks & contributions: Cory Henson, PramodAnantharam 1
  • 2. Web (and associated computing) is evolving Computing for Human Experience Enhanced Experience, Tech assimilated in life Web as an oracle / assistant / partner - “ask the Web”: using semantics to leverage Situations, 2007 text + data + services Events - Powerset, Siri, Watson Web 3.0 Objects Web ofpeople, Sensor Web - social networks, user-createdcasualcontent - 40 billionsensors Patterns Web 2.0 Web of resources - data, service, data, mashups Keywords - 4 billionmobilecomputing Web of databases 1997 - dynamically generated pages - web query interfaces Web of pages - text, manually created links Web 1.0 - extensive navigation
  • 3. Sensors everywhere ..sensing, computing, transmitting • 2009: 1.1 billion PCs, 4 billion mobile devices, 40+ billion mobile sensors (Nokia: Sensing the World with Mobile Devices) • 6 billion intelligent sensors – informed observers, rich local knowledge Christmas Bird Count 3
  • 4. Data & Knowledge Ecosystem Situational Awareness Decision Support Insight Knowledge Discovery Analysis (eg Patterns) Understanding & Perception Data Mining SSW/ W3C-SSN Search Browsing Integration OGC SWE Multimedia Data Structured, Textual Data: Scientific Literature, Web Pages, News, Blogs, Semistructured Reports, Wiki, Forums, Comments, Tweets Unstructured Observational Data Experimental Data Data Transactional Data 4
  • 5. Semantics as core enabler, enhancer @ Kno.e.sis 15 faculty ~50 PhD students Excellent Industry collaborations (MSFT, GOOG, IBM, Yahoo!, HP) Well funded Exceptional Graduates Multidisciplinary: Health/Clinical Biomedical Sc Social Sc … 5
  • 6. Semantic Models Search Integration Analysis Discovery Relationship Web Question Answering Patterns / Inference / Reasoning Situational Meta data / Awareness Semantic Annotations Metadata Extraction RDB Text Structured and Semi- Multimedia Content Sensor Data structured Data and Web Data
  • 7. From simple ontologies Knowledge Enabled Information and Services Science
  • 8. Drug Ontology Hierarchy (showing is-a relationships) formulary_ non_drug_ interaction_ property formulary reactant property indication indication_ property owl:thing monograph property _ix_class prescription interaction_ _drug_ with_non_ brandname_ prescription brand_name drug_reactant prescription individual _drug interaction _drug_ property brandname_ brandname_ composite prescription interaction_ undeclared _drug_ with_mono interaction_ generic graph_ix_cl with_prescri cpnum_ generic_ ass ption_drug group composite generic_ individual Knowledge Enabled Information and Services Science
  • 9. to complex ontologies Knowledge Enabled Information and Services Science
  • 10. N-Glycosylation metabolic pathway GNT-I attaches GlcNAc at position 2 N-glycan_beta_GlcNAc_9 N-acetyl-glucosaminyl_transferase_V N-glycan_alpha_man_4 GNT-V attaches GlcNAc at position 6 UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2 <=> UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2 UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021 Knowledge Enabled Information and Services Science
  • 11. A little bit about semantic metadata extractions and annotations Knowledge Enabled Information and Services Science
  • 12. Extractionfor Metadata Creation Nexis Digital Videos UPI AP ... ... Feeds/ Data Stores Documents WWW, Enterprise Digital Maps Repositories ... Digital Images Digital Audios Create/extract as much (semantics) metadata automatically as possible; Use ontlogies to improve and enhance EXTRACTORS extraction METADATA Knowledge Enabled Information and Services Science
  • 13. Automatic Semantic Metadata Extraction/Annotation of Textual Data Knowledge Enabled Information and Services Science
  • 14. Semantic Sensor Web Infrastructure
  • 16. Semantic Sensor ML – Adding Ontological Metadata Domain Person Ontology Company Spatial Ontology Coordinates Coordinate System Temporal Ontology Time Units Timezone Mike Botts, "SensorML and Sensor Web Enablement," 16 Earth System Science Center, UAB Huntsville
  • 17. Workflow Architecture for Managing Streaming Sensor Data
  • 18. Weather Application Weather Application Detection of events, such as blizzards, from weather station observations on LinkedSensorData 18 Demos: Real-Time Feature Streams
  • 19. SECURE: Semantics Empowered Rescue Application Weather Environment Rescue robots detect different types of fires, which may require different methods/tools to extinguish, and relays this knowledge to first responders. Demo: SECURE: Semantics Empowered Rescue Environment 19
  • 20. A Challenging Example Query What schools in Ohio should now be closed due to inclement weather? Need domain ontologies and rules to describe type of inclement weather and severity. Integrationof technologies needed to answer query 1. Spatial Aggregation 2. Semantic Sensor Web 3. Machine Perception 4. Linked Sensor Data 5. Analysis of Streaming Real-Time Data More details in: Spatial Semantics for Better Interoperability and Analysis: Challenges and Experiences in Building Semantically Rich Applications in Web 3.0 20
  • 21. Technology 1 Spatial Aggregation • What schools are in Ohio? • What weather sensors are near each of the school? 21
  • 22. Technology 2 Semantic Sensor Web (SSW) • What is inclement weather? • What sensors in Ohio are capable of detecting inclement weather? • What sensors are near schools in Ohio? • What observations are these sensors generating NOW? 22
  • 23. Technology 3 Active Machine Perception • Are these observations providing evidence for inclement weather? 23
  • 24. Technology 4 Linked Sensor Data • What schools are in Ohio? • What inclement weather necessitates school closings? • What sensors in Ohio are capable of detecting inclement weather? • What sensors are near schools in Ohio? • What observations are these sensors generating NOW? 24
  • 25. Technology 5 Analysis of Streaming Real-Time Data • What observations are these sensors generating NOW? 25
  • 27. Demos • Real-Time Feature Streams • SECURE(presentation: • SECURE: Semantics Empowered resCUe EnviRonmEnt )Amit • Trusted Perception Cycle • Sensor Discovery on Linked Data • Semantic Sensor Observation Service (SemSOS) Related Talk • Spatial Semantics for Better Interoperability and Analysis: Challenges and Experiences in Building Semantically Rich Applications in Web 3.0: Amit Sheth delivers talk at the 3rd Annual Spatial Ontology Community of Practice Workshop: Development, Implementation and Use of Geo-Spatial Ontologies and Semantics, 3 October 2010, USGS, Reston, VA.

Hinweis der Redaktion

  1. 20,000 weather stations (with ~5 sensors per station)Real-Time Feature Streams - live demo: http://knoesis1.wright.edu/EventStreams/ - video demo: https://skydrive.live.com/?cid=77950e284187e848&amp;sc=photos&amp;id=77950E284187E848%21276
  2. Automated detection of different types of fires, which each require different extinguishing methodsYouTubeSECURE Demo: http://www.youtube.com/watch?v=gHn9aCt9zQU&amp;list=UUORqXk1ZV44MOwpCorAROyQ&amp;index=8&amp;feature=plpp_video
  3. Knoesis center recently declared a center of excellence by Ohio governor
  4. Knoesis center recently declared a center of excellence by Ohio governor
  5. Knoesis center recently declared a center of excellence by Ohio governor
  6. Knoesis center recently declared a center of excellence by Ohio governor
  7. Knoesis center recently declared a center of excellence by Ohio governor