Modelling with knowledge: A review of emerging semantic approaches to environmental modelling |
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| Authors: | Ferdinando Villa Ioannis N. Athanasiadis Andrea Emilio Rizzoli |
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| Affiliation: | 1. Ecoinformatics Collaboratory, Gund Institute for Ecological Economics and Department of Plant Biology, University of Vermont, 617 Main Street, Burlington, VT, USA;2. Istituto Dalle Molle di Studi sull''Intelligenza Artificiale, Lugano, Switzerland;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, 7500 AA Enschede, The Netherlands;2. UFZ - Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, D-04318 Leipzig, Germany;3. Natural Environment Research Council, Centre for Ecology and Hydrology, Bush Estate, Penicuik, EH26 0QB, United Kingdom;4. Dynamic Macroecology, Landscape Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zurcherstrasse 111, 8903 Birmensdorf, Switzerland;5. Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH, 8092 Zurich, Switzerland;6. Department of Mechanical Engineering, School of Engineering, The University of Melbourne, Melbourne, Australia;7. Melbourne Academy for Sustainability and Society (MASS), Melbourne Sustainable Society Institute (MSSI), The University of Melbourne, Melbourne, Australia;1. CSIRO Agriculture Flagship, Australia;2. AgResearch, New Zealand;3. Alterra, Wageningen UR, Wageningen, The Netherlands;4. Democritus University of Thrace, Xanthi, Greece;5. Consiglio per la Ricerca in Agricoltura, Bologna, Italy;6. Washington State University, United States;7. USDA-ARS, United States;8. Information Technology Group, Wageningen University, Wageningen, The Netherlands;2. Irstea UR HBAN, 1 rue Pierre-Gilles de Gennes, F-92761 Antony Cedex, France;3. Basque Centre for Climate Change, BC3, Alameda Urquijo, 4 – 4°, 48008 Bilbao, Spain;4. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain;5. Graz University of Technology, Institute of Urban Water Management and Landscape Water Engineering, Stremayrgasse 10/I, 8010 Graz, Austria;6. Irstea UR MALY, centre de Lyon-Villeurbanne, F-69926 Villeurbanne Cedex, France;7. Université de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France;8. INRA, UMR 792 Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France;9. CNRS, UMR 5504, F-31400 Toulouse, France;1. School of Science, Edith Cowan University, Joondalup, WA, Australia;2. Fenner School of Environment & Society, Australian National University, Australia;3. Water and Development Research Group, Aalto University, Finland;4. Centre for Public Health, Queen''s University Belfast, Belfast, United Kingdom;5. Capability Systems Centre, School of Electrical Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy, Canberra ACT, Australia;6. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe AZ, USA;7. United States Geological Survey, Upper Midwest Water Science Center, Middleton WI, USA;8. Civil and Environmental Engineering Department, Brigham Young University, Utah, USA;9. Graham Centre for Agricultural Innovation, Charles Sturt University, Wagga Wagga, NSW, Australia;10. Department of Geology, University of Kansas, USA;11. Environmental Science Institute, Jackson School of Geosciences, University of Texas at Austin, USA |
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| Abstract: | Models, and to a lesser extent datasets, embody sophisticated statements of environmental knowledge. Yet, the knowledge they incorporate is rarely self-contained enough for them to be understood and used – by humans or machines – without the modeller's mediation. This severely limits the options in reusing environmental models and connecting them to datasets or other models. The notion of “declarative modelling” has been suggested as a remedy to help design, communicate, share and integrate models. Yet, not all these objectives have been achieved by declarative modelling in its current implementations.Semantically aware environmental modelling is a way of designing, implementing and deploying environmental datasets and models based on the independent, standardized formalization of the underlying environmental science. It can be seen as the result of merging the rationale of declarative modelling with modern knowledge representation theory, through the mediation of the integrative vision of a Semantic Web. In this paper, we review the present and preview the future of semantic modelling in environmental science: from the mediation approach, where formal knowledge is the key to automatic integration of datasets, models and analytical pipelines, to the knowledge-driven approach, where the knowledge is the key not only to integration, but also to overcoming scale and paradigm differences and to novel potentials for model design and automated knowledge discovery. |
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