A semantic annotation framework for retrieving and analyzing observational datasets

Authors:
Shawn Bowers;Huiping Cao;Mark Schildhauer;Matt Jones;Ben Leinfelder;Margaret O'Brien
Affiliations:
Gonzaga University, Spokane, WA, USA;New Mexico State University, Las Cruces, NM, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA
Venue:
ESAIR '10 Proceedings of the third workshop on Exploiting semantic annotations in information retrieval
Year:
2010

Citing 2
Cited 2

Survey of semantic annotation platforms

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A Conceptual Modeling Framework for Expressing Observational Data Semantics

ER '08 Proceedings of the 27th International Conference on Conceptual Modeling

Third workshop on exploiting semantic annotations in information retrieval (ESAIR): CIKM 2010 workshop

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Report on the third workshop on exploiting semantic annotations in information retrieval (ESAIR)

ACM SIGIR Forum

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Abstract

In many scientific disciplines, including ecology, hydrology, and earth science, scientific analysis requires access to a broad range of observational data. However, because of the amount and heterogeneity both in the structure and semantics) of observational data, approaches are needed that allow scientists to easily discover and analyze them. To address this issue, we describe a framework for accessing sobservational data. This framework combines a core observational model, domain-specific ontologies compatible with the core model, and a semantic annotation language. The annotation language provides a formal bridge between the core model and the underlying data to enable queries and analysis over annotations. The framework has been implemented to take advantage of ontology and web-based standards, and has also been integrated within a popular metadata tool for managing ecological datasets.