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

• Published in: Future generation computer systems Vol. 36; pp. 430 - 440
• Main Authors: Patton, Evan W., Seyed, Patrice, Wang, Ping, Fu, Linyun, Dein, F. Joshua, Bristol, R. Sky, McGuinness, Deborah L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2014
• Subjects: Ecological data integration; Provenance; Semantic environmental informatics; Semantic web; Semantic web; Semantic environmental informatics; Provenance; Ecological data integration
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2013.09.017

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. •Discusses the need for flexible frameworks to handle data integration.•Proposes a semantic solution to integrate environmental data for resource managers.•Discusses interoperability challenges addressed with existing ontologies.•Analyzes performance of different schema for artificial intelligence reasoning.•Compares OWL-DL reasoning with more lightweight rule-based reasoning.