In previous work we implemented a prototype of an ontology-based semantic enrichment and mediation system for flower-visiting data digitized from labels of flower-visiting insect specimens in natural history collections. This system transformed database records documenting physical specimens into enriched records of ecological events e.g. a FlowerUtilizingEvent or a FlowerProductUtilizingEvent. In subsequent work we created a probabilistic model (Bayesian Network) of the causal knowledge that an expert implicitly uses to interpret individual specimen records e.g. to assert that an insect was probably foraging for pollen or probably foraging for nectar. The objective of the present work is to link interpretations of individual organisms behaviour (i.e. output from the Bayesian Network) to aggregated records of behavioral interactions. These aggregations are population samples which allow the data to be interpreted at a higher level of abstraction (population and community level) i.e. in terms of ecological relationships between population samples of different species. Using the widely adopted modelling construct of the Interaction Network (represented by an ontology), we modelled an ecological community as a network of interacting populations of different species. Each node in the interaction network is a population sample of a different species. Nodes are connected by edges representing ecological interactions, of which there are several types. We envisage a system that will allow a user to filter database records by spatio-temporal extent so as to realistically model a network of co-existing populations. The user will then be able to adjust the level of precision of the visualised ecological interactions (e.g. ecologicalInteraction > foragingEcologicalInteraction > nectarForagingEcologicalInteraction). Explicit semantics could bring a degree of standardization to the construction of interaction networks and the interpretation of flower-visiting data.
