Title: Indirectly driven knowledge modelling in ecology
Authors: Deana D. Pennington, Ioannis N. Athanasiadis, Shawn Bowers, Serguei Krivov, Joshua Madin, Mark Schildhauer, Ferdinando Villa
Addresses: Department of Biology, University of New Mexico, MSC03 2020, Albuquerque, New Mexico 87131-0001, USA. ' Istituto Dalle Molle di Studi, sull'Intellienza Artificiale, Manno, Lugano, Switzerland. ' University of California at Davis Genome Center, 451 Health Sciences Drive, Davis, California 95616-8816, USA. ' University of Vermont, 617 Main St., Burlington, Vermont 05405, USA. ' Department of Biological Sciences, Macquarie University, N.S.W. 2109, Australia. ' National Center for Ecological Analysis and Synthesis, University of California at Santa Barbara, 735 State Street, Suite 300, Santa Barbara, California 93101, USA. ' University of Vermont, 617 Main St., Burlington, Vermont 05405, USA
Abstract: We describe collaborative efforts among a group of Knowledge Representation (KR) experts, domain scientists, and scientific information managers in developing knowledge models for ecological and environmental concepts. The development of formal, structured approaches to KR used by the group (i.e., ontologies) can be informed by evidence marshalled from unstructured approaches to KR and semantic tagging already in use by the community.
Keywords: indirectly-driven knowledge modelling; ecological knowledge models; ecological metadata; scientific observation; ontologies; knowledge representation; semantic tagging.
International Journal of Metadata, Semantics and Ontologies, 2008 Vol.3 No.3, pp.210 - 225
Published online: 28 Feb 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article