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Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113002/ https://www.ncbi.nlm.nih.gov/pubmed/27851814 http://dx.doi.org/10.1371/journal.pone.0166559 |
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author | Coetzer, Willem Moodley, Deshendran Gerber, Aurona |
author_facet | Coetzer, Willem Moodley, Deshendran Gerber, Aurona |
author_sort | Coetzer, Willem |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5113002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-51130022016-12-08 Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data Coetzer, Willem Moodley, Deshendran Gerber, Aurona PLoS One Research Article 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. Public Library of Science 2016-11-16 /pmc/articles/PMC5113002/ /pubmed/27851814 http://dx.doi.org/10.1371/journal.pone.0166559 Text en © 2016 Coetzer et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Coetzer, Willem Moodley, Deshendran Gerber, Aurona Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data |
title | Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data |
title_full | Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data |
title_fullStr | Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data |
title_full_unstemmed | Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data |
title_short | Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data |
title_sort | eliciting and representing high-level knowledge requirements to discover ecological knowledge in flower-visiting data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113002/ https://www.ncbi.nlm.nih.gov/pubmed/27851814 http://dx.doi.org/10.1371/journal.pone.0166559 |
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