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. 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 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

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.

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

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_UNIT

34.07 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 Script

Retrieval 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 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>

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!