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Semantic Water Quality - Ping Wang
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Semantic Water Quality - Ping Wang

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  • Tetherless World Constellation Semantic Water Quality Portal (TWC-SWQP) is both a water quality portal application and an example of a semantic approach to environmental informatics applications. Our integration scheme uses a core domain ontology and integrates water data from different authoritative sources along with multiple regulation ontologies to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. How to: Input 02888 Selected the facets as shown Click “Go!” To get the pop up window, click the polluted water source
  • How to: In the “Regulation” box, check the “CA Regulation”, and Click “Go” Results: We can see that there are more polluted water sites, polluting facilities based on CA Regulation ”.
  • How to: Unselect “No Filter” Click “select” at the next row and select one or more characteristics from the pop up window Click “Go” Result: There are less polluting facilities and no polluted water sources, since we only select characteristic to be phosphorus_total_as_P. So all polluting facilities displayed are facilities releasing over limit amount of phosphorus_total_as_P.
  • How to: Click on one polluting facility and you will see the pop up window for pollution facts
  • How to: Click the “?” near the “measured value” in the pop up window for pollution facts Result: The provenance includes: the pml file, the RDF file and the original source file
  • How to: Click the “?” near the “limit value” in the pop up window for pollution facts Result: The provenance includes: the pml file, the RDF file and the original source file
  • How to: Click the “Visualize Characteristics” at the bottom of the pop up window for pollution facts Select the permit for the facility (one facility can have multiple permits), the characteristic, the test type Click “click” Result: The violations are highlighted Move the mouse near the data point, the measurement time and value appear.
  • Most Probable Number (MPN)
  • Each data integration stage involves different provenance. For the water quality data, the portal automatically captures provenance and encodes them in PML2 via csv2rdf4lod.
  • The water quality regulations are converted to OWL2 ontologies with our ad-hoc regulation converter. The regulation provenance data are captured manually. We plan to improve our regulation converter to automate the regulation provenance capture.
  • Transcript

    • 1. TWC-SWQP: A Semantically-Enabled Provenance-Aware Water Quality Portal Ping Wang, Jin Guang Zheng, Linyun Fu, Evan W. Patton, Timothy Lebo, Li Ding, Joanne S. Luciano, Deborah L. McGuinness Tetherless World Constellation RPI
    • 2. Outline
      • Introduction
      • Data Sources
      • Semantic Web Approach
      • Future Work
    • 3. Outline
      • Introduction
      • Data Sources
      • Semantic Web Approach
      • Future Work
    • 4. SWQP Overview
    • 5. Apply CA Regulation
    • 6. Retrieval by Characteristic
    • 7. Detailed polluting facility
    • 8. Provenance of water data
    • 9. Provenance of regulations
    • 10. Measurement Visualization
    • 11. Outline
      • Introduction
      • Data Sources
      • Semantic Web Approach
      • Future Work
    • 12. Data Sources Data Type Data Source Water Quality Data EPA Enforcement & Compliance History Online (ECHO) Database USGS National Water Information System (NWIS) Water-Quality Web Services Water Quality Regulation EPA (National Water Regulation) California Code of Regulations Massachusetts Department of Environmental Protection New York Department of Health State of Rhode Island Department of Environmental Management
    • 13. Outline
      • Introduction
      • Data Sources
      • Semantic Web Approach
      • Future Work
    • 14. Domain Knowledge Modeling
      • Core ontology design 1
      1 http://purl.org/twc/ontology/swqp/core
    • 15. Domain Knowledge Modeling
      • Regulation ontology design 2
      2 e.g., http://purl.org/twc/ontology/swqp/region/ny and http://purl.org/twc/ontology/swqp/region/ri; others are listed at http://purl.org/twc/ontology/swqp/region/
    • 16. Reasoning Domain Data with Regulations
      • Combining the water measurement data, the core and regulation ontologies, a reasoner can decide if a water body is polluted using OWL2 classification.
      Benefits The core ontology is small: 18 classes, 4 object properties, and 10 data properties. The ontology component can be easily extended to incorporate more regulations Flexible querying and reasoning: the user can select the regulation to apply
    • 17. Data Integration
      • We used the open source tool csv2rdf4lod 3,4 .
        • Linking ontological terms
        • Aligning instance references
        • Converting complex objects
      3 Lebo, T., Williams, G.T., 2010. Converting governmental datasets into linked data. Proceedings of the 6th International Conference on Semantic Systems, I-SEMANTICS ’10, pp. 38:1–38:3. 4 http://purl.org/twc/id/software/csv2rdf4lod C1_VALUE C1_UNIT C2_VALUE C2_UNIT 34.07 MPN/100ML 53.83 MPN/100ML
    • 18. Provenance Support
      • Provenance Capture
      • Provenance Usage
        • Data Source Widget
        • Data Trace Visualization
    • 19. Water Data Provenance Capture Integration State Provenance Script Retrieval source URL, modification time, inference engine, inference rule, involved actor purl.sh Adjust antecedent data, modification time inference engine, inference rule, involved actor punzip.sh justify.sh Convert antecedent data, invocation time, inference engine, interpretation rule convert*.sh (conversion trigger) Publish URL of published dump file, publish time, involved actor publish.sh
    • 20. Water Regulation Provenance Capture See complete table at http://tw.rpi.edu/web/project/TWC-SWQP/compare_five_regulation
    • 21. Water Regulation Provenance Capture See complete table at http://tw.rpi.edu/web/project/TWC-SWQP/compare_five_regulation
    • 22. Data Source Widget Input URL of SPARQL endpoint and (optional) list of its named graphs, and name of the SimpleNamedGraphSourceGraph instance Output SimpleNamedGraphSourceGraph instance filled with simple descriptions of the source organizations responsible for the data Process Walk a big provenance graph for each named graph and abstracts it into one triple: <data_1> dct:source <source_1>
    • 23. Data Source Widget
      • Usage
        • Presentation of the data sources on the interface
        • Source based data retrieval
    • 24. Provenance Visualization
    • 25. Future Work
      • Convert data and encode the regulations for the remaining states
      • Linking to Health Domain
      • Utilize data from other sources, e.g. weather and flood forecasts
      • Apply this architecture to other applications, e.g. the Clean Air Status and Trends demo 5
      5 http://logd.tw.rpi.edu/demo/clean_air_status_and_trends_-_ozone
    • 26.
      • Thank you!

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