Metadata-supported Automated Ecological Modelling

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, automatically to generate prototypical partial models to be further improved by the modeller.