causalizeR: a text mining algorithm to identify causal relationships in scientific literature
Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using dat...
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crpeerj:10.7717/peerj.11850 2024-06-02T08:15:26+00:00 causalizeR: a text mining algorithm to identify causal relationships in scientific literature Ancin-Murguzur, Francisco J. Hausner, Vera H. Fram Center Flagship Effects of Climate Change on Ecosystems, Landscape Local Communities and Indigenous People Project EcoShift Future ArcTic Ecosystems UiT The Arctic University of Norway 2021 http://dx.doi.org/10.7717/peerj.11850 https://peerj.com/articles/11850.pdf https://peerj.com/articles/11850.xml https://peerj.com/articles/11850.html en eng PeerJ https://creativecommons.org/licenses/by/4.0/ PeerJ volume 9, page e11850 ISSN 2167-8359 journal-article 2021 crpeerj https://doi.org/10.7717/peerj.11850 2024-05-07T14:13:26Z Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR ( https://github.com/fjmurguzur/causalizeR ), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem. Article in Journal/Newspaper Tundra PeerJ Publishing PeerJ 9 e11850 |
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Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR ( https://github.com/fjmurguzur/causalizeR ), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem. |
author2 |
Fram Center Flagship Effects of Climate Change on Ecosystems, Landscape Local Communities and Indigenous People Project EcoShift Future ArcTic Ecosystems UiT The Arctic University of Norway |
format |
Article in Journal/Newspaper |
author |
Ancin-Murguzur, Francisco J. Hausner, Vera H. |
spellingShingle |
Ancin-Murguzur, Francisco J. Hausner, Vera H. causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
author_facet |
Ancin-Murguzur, Francisco J. Hausner, Vera H. |
author_sort |
Ancin-Murguzur, Francisco J. |
title |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_short |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_full |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_fullStr |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_full_unstemmed |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_sort |
causalizer: a text mining algorithm to identify causal relationships in scientific literature |
publisher |
PeerJ |
publishDate |
2021 |
url |
http://dx.doi.org/10.7717/peerj.11850 https://peerj.com/articles/11850.pdf https://peerj.com/articles/11850.xml https://peerj.com/articles/11850.html |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
PeerJ volume 9, page e11850 ISSN 2167-8359 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.7717/peerj.11850 |
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PeerJ |
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9 |
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e11850 |
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1800739603918880768 |