TWC-SWQP: A Semantically-Enabled Provenance-Aware Water
Quality PortalPing Wang, Jin Guang Zheng, Linyun Fu, Evan W. Patton, Timothy Lebo, Li Ding,
Joanne S. Luciano, Deborah L. McGuinnessTetherless World Constellation
RPI
Outline
• Introduction• Data Sources• Semantic Web Approach• Future Work
Outline
• Introduction• Data Sources• Semantic Web Approach• Future Work
SWQP Overview
Apply CA Regulation
Retrieval by Characteristic
Detailed polluting facility
Provenance of water data
Provenance of regulations
Measurement Visualization
Outline
• Introduction• Data Sources• Semantic Web Approach• Future Work
Data Sources
Data Type Data SourceWater 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
Outline
• Introduction• Data Sources• Semantic Web Approach• Future Work
Domain Knowledge Modeling
• Core ontology design1
1 http://purl.org/twc/ontology/swqp/core
Domain Knowledge Modeling
• Regulation ontology design2
2e.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/
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.
BenefitsThe core ontology is small: 18 classes, 4 object properties, and 10 data properties.The ontology component can be easily extended to incorporate more regulationsFlexible querying and reasoning: the user can select the regulation to apply
Data Integration
• We used the open source tool csv2rdf4lod3,4.– Linking ontological terms– Aligning instance references– Converting complex objects
C1_VALUE C1_UNIT C2_VALUE C2_UNIT34.07 MPN/100ML 53.83 MPN/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
Provenance Support
• Provenance Capture• Provenance Usage
– Data Source Widget– Data Trace Visualization
Water Data Provenance Capture
Integration State Provenance ScriptRetrieval source URL, modification time,
inference engine, inference rule,involved actor
purl.sh
Adjust antecedent data, modification timeinference engine, inference rule,involved actor
punzip.shjustify.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
Water Regulation Provenance Capture
See complete table at http://tw.rpi.edu/web/project/TWC-SWQP/compare_five_regulation
Water Regulation Provenance Capture
See complete table at http://tw.rpi.edu/web/project/TWC-SWQP/compare_five_regulation
Data Source WidgetInput 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>
Data Source Widget
• Usage• Presentation of the data sources on
the interface• Source based data retrieval
Provenance Visualization
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 demo5
5 http://logd.tw.rpi.edu/demo/clean_air_status_and_trends_-_ozone
• Thank you!
