Eliciting and representing high-level knowledge requirements to discover ecological knowledge in flower-visiting data

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Authors

Coetzer, Willem
Moodley, Deshendran
Gerber, Aurona Jacoba

Journal Title

Journal ISSN

Volume Title

Publisher

Public Library of Science

Abstract

Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.

Description

S1 File. Elicitation of expert knowledge. Experts were asked to read an explanation of how a Bayesian network can be used to represent knowledge, and then answer questions as to the completeness of the presented model and whether the results of running the Bayesian network using sample data were reasonable.

Keywords

Knowledge, Flower, Plant organisms, Insect, Knowledge requirements, Ecological knowledge, Flower-visiting data, Individual organisms (data), Behavioral interactions

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Citation

Coetzer W, Moodley D, Gerber A (2016) Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data. PLoS ONE 11(11): e0166559. DOI: 10.1371/journal.pone.0166559.