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Semantic Water Quality - Ping Wang

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Semantic Water Quality - Ping Wang

  1. 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. 2. Outline <ul><li>Introduction </li></ul><ul><li>Data Sources </li></ul><ul><li>Semantic Web Approach </li></ul><ul><li>Future Work </li></ul>
  3. 3. Outline <ul><li>Introduction </li></ul><ul><li>Data Sources </li></ul><ul><li>Semantic Web Approach </li></ul><ul><li>Future Work </li></ul>
  4. 4. SWQP Overview
  5. 5. Apply CA Regulation
  6. 6. Retrieval by Characteristic
  7. 7. Detailed polluting facility
  8. 8. Provenance of water data
  9. 9. Provenance of regulations
  10. 10. Measurement Visualization
  11. 11. Outline <ul><li>Introduction </li></ul><ul><li>Data Sources </li></ul><ul><li>Semantic Web Approach </li></ul><ul><li>Future Work </li></ul>
  12. 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. 13. Outline <ul><li>Introduction </li></ul><ul><li>Data Sources </li></ul><ul><li>Semantic Web Approach </li></ul><ul><li>Future Work </li></ul>
  14. 14. Domain Knowledge Modeling <ul><li>Core ontology design 1 </li></ul>1 http://purl.org/twc/ontology/swqp/core
  15. 15. Domain Knowledge Modeling <ul><li>Regulation ontology design 2 </li></ul>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. 16. Reasoning Domain Data with Regulations <ul><li>Combining the water measurement data, the core and regulation ontologies, a reasoner can decide if a water body is polluted using OWL2 classification. </li></ul>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. 17. Data Integration <ul><li>We used the open source tool csv2rdf4lod 3,4 . </li></ul><ul><ul><li>Linking ontological terms </li></ul></ul><ul><ul><li>Aligning instance references </li></ul></ul><ul><ul><li>Converting complex objects </li></ul></ul>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. 18. Provenance Support <ul><li>Provenance Capture </li></ul><ul><li>Provenance Usage </li></ul><ul><ul><li>Data Source Widget </li></ul></ul><ul><ul><li>Data Trace Visualization </li></ul></ul>
  19. 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. 20. Water Regulation Provenance Capture See complete table at http://tw.rpi.edu/web/project/TWC-SWQP/compare_five_regulation
  21. 21. Water Regulation Provenance Capture See complete table at http://tw.rpi.edu/web/project/TWC-SWQP/compare_five_regulation
  22. 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. 23. Data Source Widget <ul><li>Usage </li></ul><ul><ul><li>Presentation of the data sources on the interface </li></ul></ul><ul><ul><li>Source based data retrieval </li></ul></ul>
  24. 24. Provenance Visualization
  25. 25. Future Work <ul><li>Convert data and encode the regulations for the remaining states </li></ul><ul><li>Linking to Health Domain </li></ul><ul><li>Utilize data from other sources, e.g. weather and flood forecasts </li></ul><ul><li>Apply this architecture to other applications, e.g. the Clean Air Status and Trends demo 5 </li></ul>5 http://logd.tw.rpi.edu/demo/clean_air_status_and_trends_-_ozone
  26. 26. <ul><li>Thank you! </li></ul>
  • mox601

    Sep. 6, 2011

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