Combining Machine Learning & Reasoning for Biodiversity Data Intelligence

Authors

  • Atriya Sen University of New Orleans
  • Beckett Sterner Arizona State University
  • Nico Franz Arizona State University
  • Caleb Powel Arizona State University
  • Nathan Upham Arizona State University

Keywords:

Environmental Sustainability, Natural Sciences, Agriculture/Food

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.

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Published

2021-05-18

How to Cite

Sen, A., Sterner, B., Franz, N., Powel, C., & Upham, N. (2021). Combining Machine Learning & Reasoning for Biodiversity Data Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14911-14919. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17750

Issue

Section

AAAI Special Track on AI for Social Impact