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 TypeData Source Water Quality DataEPA 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 C1_VALUEC1_UNITC2_VALUEC2_UNIT 34.07MPN/100ML53.83MPN/100ML 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

18. Provenance Support Provenance Capture Provenance Usage –Data Source Widget –Data Trace Visualization

19. Water Data Provenance Capture Integration StateProvenanceScript Retrievalsource URL, modification time, inference engine, inference rule, involved actor purl.sh Adjustantecedent data, modification time inference engine, inference rule, involved actor punzip.sh justify.sh Convertantecedent data, invocation time, inference engine, interpretation rule convert*.sh (conversion trigger) PublishURL 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 InputURL of SPARQL endpoint and (optional) list of its named graphs, and name of the SimpleNamedGraphSourceGraph instance OutputSimpleNamedGraphSourceGraph instance filled with simple descriptions of the source organizations responsible for the data ProcessWalk a big provenance graph for each named graph and abstracts it into one triple: dct:source

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!