Eliciting and representing high-level knowledge requirements to discover ecological knowledge in flower-visiting data
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Date
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
Sustainable Development Goals
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.