Defining projects and scenarios for integrated assessment modelling using ontology
This paper explains our experiences with a challenging and time-consuming task, e.g. arriving at a shared understanding on the definition of projects, experiments and scenarios among researchers coming from different disciplines, who have been exposed to dissimilar education and research experience. We demonstrate the use of ontologies in building this shared set of definitions and the relationship between the ontology and the human computer interaction through a case study. With a common ontology that represents the joint conceptualization of the projects, experiments and scenarios each researcher can refer at any later stage to the semantics of the concepts used. A collaborative approach was used to build such a common ontology in the SEAMLESS-Integrated Project, funded through the EU sixth Framework Programme, which aims at developing an integrated modelling framework (SEAMLESS-IF) to assess, ex-ante, agricultural and environmentalpolicy options, allowing cross-scale analysis of a broad range of sustainability issues. As a first validation of the project ontology, a set of four fictitious sample projects were made. One of these sample projects is an integrated assessment for one region Midi-Pyrénées in the South of France concerning the impacts of the CAP2003 reform, which is described in this paper.
S. Janssen, J. Wien, H. Li, I. N. Athanasiadis, F. Ewert, M. Knapen, D. Huber, O. Thérond, A. Rizzoli, H. Belhouchette, M. Svensson, M. van Ittersum, Defining projects and scenarios for integrated assessment modelling using ontology, MODSIM 2007 Intl Congress on Modelling and Simulation, pg. 2055-2061, 2007, Modelling and Simulation Society of Australia and New Zealand.
You might also enjoy (View all publications)
- Storing, combining and analysing turkey experimental data in the Big Data era
- Enabling reusability of plant phenomic datasets with MIAPPE 1.1
- Defining and classifying infrastructural contestation: Towards a synergy between anthropology and data science