7. Conclusion
We have demonstrated that heterogeneous arthropod specimen data containing flower-visiting observations can be transformed into useful ecological information by representing behavioral VSV-G tag knowledge using ontologies, and that semantic enrichment and mediation can be automated. Assumptions were unavoidable in remedying the problem of missing data, but these were substantiated by accepted knowledge. Moreover, the knowledge model allowed the user to ignore enrichment that relied on assumptions.
Future work will involve building a new system layer to link the ecological interactions and their constituent events into a plant-visiting or flower-visiting ecological interaction network parietal lobe will summarize the essential information in a way that is objective, enriched, standardized, consistent (i.e. semantically enriched and semantically mediated) and of a high quality (i.e. integrating expert- and implicit knowledge). Such a flower-visiting network could take the form of a directed acyclic graph representing a Bayesian network, which will, moreover, allow the user to model, and reason with, uncertainty in flower-visiting data.
We have demonstrated that heterogeneous arthropod specimen data containing flower-visiting observations can be transformed into useful ecological information by representing behavioral VSV-G tag knowledge using ontologies, and that semantic enrichment and mediation can be automated. Assumptions were unavoidable in remedying the problem of missing data, but these were substantiated by accepted knowledge. Moreover, the knowledge model allowed the user to ignore enrichment that relied on assumptions.
Future work will involve building a new system layer to link the ecological interactions and their constituent events into a plant-visiting or flower-visiting ecological interaction network parietal lobe will summarize the essential information in a way that is objective, enriched, standardized, consistent (i.e. semantically enriched and semantically mediated) and of a high quality (i.e. integrating expert- and implicit knowledge). Such a flower-visiting network could take the form of a directed acyclic graph representing a Bayesian network, which will, moreover, allow the user to model, and reason with, uncertainty in flower-visiting data.