Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Metadata-Supported Automated Ecological Modelling

Metadata-Supported Automated Ecological Modelling
View Sample PDF
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


View Metadata-Supported Automated Ecological Modelling on the publisher's website for pricing and purchasing information.


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

Thinh Gia Hoang, Giang Ngo Tinh Nguyen, Dat Anh Le. © 2022. 19 pages.
Niranjan Devkota, Rekha Rai, Ghanashyam Khanal, Ihtsham Ul Haq Padda, Udaya Raj Paudel, Seeprata Parajuli, Udbodh Bhandari. © 2022. 22 pages.
Hasan Dinçer, Serhat Yüksel, Hüsne Karakuş. © 2022. 14 pages.
Fatih Kayhan. © 2022. 13 pages.
Manuel Antonio Fernández-Villacañas Marín, Ignacio Fernández-Villacañas Marcos. © 2022. 21 pages.
S. Su Baysal, M. Ali Ülkü. © 2022. 19 pages.
Fernando Almeida, Nuno Miranda, Bruno Vieira. © 2022. 22 pages.
Body Bottom