SemantEco: A semantically powered modular architecture for integrating distributed environmental and ecological data

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Abstract:

We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of linked open data. In previous work, we designed and implemented a semantically enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. In this work, we discuss SemantEco’s new architecture that supports modular extensions and makes it easier to support additional domains. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. We conclude with a discussion of how, through the application of semantic technologies, modular designs will make it easier for resource managers to bring in new sources of data to support more complex use cases.

History

DateCreated ByLink
February 24, 2014
16:58:56
Xixi LuoDownload
February 24, 2014
16:07:35
Xixi LuoDownload
February 24, 2014
15:54:57
Xixi LuoDownload
February 24, 2014
15:29:28
Xixi LuoDownload

Related Projects:

SemantEco Annotator Project LogoSemantEco Annotator
Principal Investigator: Deborah L. McGuinness
Co Investigator: Patrice Seyed
Description: Generating useful RDF linked data is not a straightforward process for scientists using today's tools. In this project we introduce the SemantEco Annotator, a semantic web application that leverages community-based vocabularies and ontologies during the translation process itself to ease the process of drawing out implicit relationships in tabular data so that they may be immediately available for use within the LOD cloud. Our goal for the SemantEco Annotator is to make advanced RDF translation techniques available to the layperson.
SemantAQUA LogoSemantic Water Quality Portal (SemantAQUA)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Joanne S. Luciano
Description: We present a semantic technology-based approach to emerging environmental information systems. We used our linked data approach in the Tetherless World Constellation Semantic Water Quality Portal (TWC-SWQP). Our integration scheme uses a core domain ontology and integrates water data from different authoritative sources along with multiple regulation ontologies to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach also captures and leverages provenance to improve transparency. In addition, semantic water quality portal features provenance-based facet generation, query answering and data validation over the integrated data via SPARQL. We introduce the approach and the water portal, and highlight some of its potential impacts for the future of environmental monitoring systems.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts:
Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts: Semantic Web
Ontology Engineering Environments
Lead Professor: Deborah L. McGuinness
Description: Ontology Engineering Environments
Concepts: Semantic Web