Combining Machine Learning & Reasoning for Biodiversity Data Intelligence
Abstract
The current crisis in global natural resource management makes it imperative that we better leverage the vast data sources associated with taxonomic entities (such as recognized species of plants and animals), which are known collectively as biodiversity data. However, these data pose considerable challenges for artificial intelligence: while growing rapidly in volume, they remain highly incomplete for many taxonomic groups, often show conflicting signals from different sources, and are multi-modal and therefore constantly changing in structure. In this paper, we motivate, describe, and present a novel workflow combining machine learning and automated reasoning, to discover patterns of taxonomic identity and change - i.e. “taxonomic intelligence” - leading to scalable and broadly impactful AI solutions within the bio-data realm.
Cite
Text
Sen et al. "Combining Machine Learning & Reasoning for Biodiversity Data Intelligence." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17750Markdown
[Sen et al. "Combining Machine Learning & Reasoning for Biodiversity Data Intelligence." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/sen2021aaai-combining/) doi:10.1609/AAAI.V35I17.17750BibTeX
@inproceedings{sen2021aaai-combining,
title = {{Combining Machine Learning & Reasoning for Biodiversity Data Intelligence}},
author = {Sen, Atriya and Sterner, Beckett W. and Franz, Nico M. and Powel, Caleb and Upham, Nathan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {14911-14919},
doi = {10.1609/AAAI.V35I17.17750},
url = {https://mlanthology.org/aaai/2021/sen2021aaai-combining/}
}