Semantic integration of government data for water quality management

Chen, Zhiyuan; Gangopadhyay, Arrya; Holden, Stephen H.; Karabatis, George; McGuire, Michael P.

doi:10.1016/j.giq.2007.04.004

Cited by 28 publications

(16 citation statements)

References 22 publications

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“…The insight in the form of brining the review of the literature on e-governance and underlying data integration and semantic capability provision thus established a linkage between advanced levels of e-government and underlying data integration for users external to government, stakeholders and policy makers [42].…”

Section: Discussionmentioning

confidence: 99%

Semantic Data Integration Approaches for E-Governance

Al-Sudairy¹,

Vasista²

2011

IJWesT

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Section: Discussionmentioning

confidence: 99%

Semantic Data Integration Approaches for E-Governance

Al-Sudairy¹,

Vasista²

2011

IJWesT

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“…Raskin et al [13] propose the SWEET ontology family for Earth system science. Chen et al [5] models relationships among water quality datasets. Chau et al [4] models a specific aspect of water quality.…”

Section: Modeling Domain Knowledge For Environmental Monitoringmentioning

confidence: 99%

“…Chen et al [5] proposed a prototype system that integrates water quality data from multiple sources and retrieves data using semantic relationships among data. Chau [4] presented an ontology-based knowledge management system (KMS) to enable novice users to find numerical flow and water quality models given a set of constraints.…”

Section: Related Workmentioning

confidence: 99%

A Semantic Portal for Next Generation Monitoring Systems

Wang

Zheng

et al. 2011

The Semantic Web – ISWC 2011

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Abstract. We present a semantic technology-based approach to emerging monitoring systems based on our linked data approach in the Tetherless World Constellation Semantic Ecology and Environment Portal (SemantEco). Our integration scheme uses an upper level monitoring ontology and mid-level monitoring-relevant domain ontologies. The initial domain ontologies focus on water and air quality. We then integrate domain data from different authoritative sources and multiple regulation ontologies (capturing federal as well as state guidelines) to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach captures and leverages provenance to enable transparency. In addition, SemantEco features provenance-based facet generation, query answering, and validation over the integrated data via SPARQL. We introduce the general SemantEco approach, describe the implementation which has been built out substantially in the water domain creating the SemantAqua portal, and highlight some of the potential impacts for the future of semantically-enabled monitoring systems.

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“…More recently, a trend toward online query access has emerged, and some biological databases now provide remote access to their content in standardized formats such as XML (extensible markup language), which can be queried by languages such as XQuery or Xcerpt. For example, Chen et al (2007) approached data integration by converting ecological metadata from all sources to an Ecological Metadata Language (EML) format, a well-known metadata standard developed by and for the ecology community. EML was chosen for 2 reasons: 1) EML has been used in several well-known projects (Knowledge Network for Biocomplexity [KNB], Scientific Environment for Ecological Knowledge [SEEK]) for metadata integration because an extensive EML terminology currently exists, and 2) EML is executed as a series of XML document types.…”

Section: Traits and The Promise Of Web 20 Technologiesmentioning

confidence: 99%

“…EML was chosen for 2 reasons: 1) EML has been used in several well-known projects (Knowledge Network for Biocomplexity [KNB], Scientific Environment for Ecological Knowledge [SEEK]) for metadata integration because an extensive EML terminology currently exists, and 2) EML is executed as a series of XML document types. Unfortunately, conversion of metadata to EML is currently possible by only manual techniques (Chen et al 2007). An even smaller number of biological databases, notably Swiss-Prot, YeastHub, and Linkhub, have content available in semantically rich, knowledge representational formats such as RDF (Resource Description Framework) and OWL (Ontology Web Language).…”

Section: Traits and The Promise Of Web 20 Technologiesmentioning

confidence: 99%

Toward a knowledge infrastructure for traits‐based ecological risk assessment

Baird

Baker

Brua

et al. 2010

Integr Envir Assess & Manag

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The trait approach has already indicated significant potential as a tool in understanding natural variation among species in sensitivity to contaminants in the process of ecological risk assessment. However, to realize its full potential, a defined nomenclature for traits is urgently required, and significant effort is required to populate databases of species-trait relationships. Recently, there have been significant advances in the area of information management and discovery in the area of the semantic web. Combined with continuing progress in biological trait knowledge, these suggest that the time is right for a reevaluation of how trait information from divergent research traditions is collated and made available for end users in the field of environmental management. Although there has already been a great deal of work on traits, the information is scattered throughout databases, literature, and undiscovered sources. Further progress will require better leverage of this existing data and research to fill in the gaps. We review and discuss a number of technical and social challenges to bringing together existing information and moving toward a new, collaborative approach. Finally, we outline a path toward enhanced knowledge discovery within the traits domain space, showing that, by linking knowledge management infrastructure, semantic metadata (trait ontologies), and Web 2.0 and 3.0 technologies, we can begin to construct a dedicated platform for TERA science.

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Semantic integration of government data for water quality management

Cited by 28 publications

References 22 publications

Semantic Data Integration Approaches for E-Governance

Semantic Data Integration Approaches for E-Governance

A Semantic Portal for Next Generation Monitoring Systems

Toward a knowledge infrastructure for traits‐based ecological risk assessment

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