TY - JOUR AU - Sen, Atriya AU - Sterner, Beckett AU - Franz, Nico AU - Powel, Caleb AU - Upham, Nathan PY - 2021/05/18 Y2 - 2024/03/28 TI - Combining Machine Learning & Reasoning for Biodiversity Data Intelligence JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 17 SE - AAAI Special Track on AI for Social Impact DO - 10.1609/aaai.v35i17.17750 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17750 SP - 14911-14919 AB - 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. ER -