The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Metadata-Supported Automated Ecological Modelling
|
|
Author(s): Virginia Brilhante (University of Edinburgh, UK, and University of Amazonas, Brazil)and Dave Robertson (University of Edinburgh, UK)
Copyright: 2001
Pages: 20
Source title:
Environmental Information Systems in Industry and Public Administration
Source Author(s)/Editor(s): Claus Rautenstrauch (Otto von Guericke University, Denmark)and Susanne Patig (Otto-von-Guericke University Magdeburg, Germany)
DOI: 10.4018/978-1-930708-02-0.ch021
Purchase
|
Abstract
Ecological models should be rooted in data derived from observation, allowing methodical model construction and clear accounts of model results with respect to the data. Unfortunately, many models are retrospectively fitted to data because in practice it is difficult to bridge the gap between concrete data and abstract models. Our research is on automated methods to support bridging this gap. The approach proposed consists of raising the data level of abstraction via an ecological metadata ontology and from that, through logic-based knowledge representation and inference, to automatically generate prototypical partial models to be further improved by the modeler. In this chapter we aim to: 1) give an overview of current automated modelling approaches applied to ecology, and relate them to our metadata-based approach under investigation; and 2) explain and demonstrate how it is realized using logic-based formalisms. We give the overview of current automated modelling approaches in the section “Ecological Modeling and Automation: Current Approaches,” focusing on compositional modelling and model induction. The contrast between these and our approach, where we adopt metadata descriptions through an ontology and logic-based modelling, is discussed in the section “Our Automated Ecological Modelling Avenue.” The next section, “Towards a System for Metadata–Supported Automated Modeling,” makes ideas more concrete, starting with further details on the Ecolingua ontology, followed by examples of automated model structuring and parameter estimation. In the concluding section, “A Look Ahead and Conclusion,” we comment briefly on the ontologies trend and on the outlook of our research.
Related Content
|
Himanshi Srivastava, Pinki Saini, Anchal Singh, Sangeeta Yadav.
© 2024.
38 pages.
|
|
Rakesh Dutta, Jayashri Dutta.
© 2024.
16 pages.
|
|
Sudha Subburaj, A. Lakshmi Kanthan Bharathi.
© 2024.
30 pages.
|
|
Hari Shankar Biswas, Sandeep Poddar.
© 2024.
15 pages.
|
|
Mihaela Rosca, Petronela Cozma, Maria Gavrilescu.
© 2024.
35 pages.
|
|
Indranee Changmai.
© 2024.
28 pages.
|
|
Periasamy Palanisamy, M. Kumaresan, M. Maheswaran.
© 2024.
19 pages.
|
|
|