Combining Machine Learning & Reasoning for Biodiversity Data Intelligence
Combining Machine Learning & Reasoning for Biodiversity Data Intelligence
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DOI：
10.1609/aaai.v35i17.17750
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发表时间：
2021-05
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影响因子：
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通讯作者：
Atriya Sen;B. Sterner;N. Franz;Caleb Powel;Nate Upham
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作者：
Atriya Sen;B. Sterner;N. Franz;Caleb Powel;Nate Upham
当前的全球自然资源管理危机使我们必须更好地利用与分类实体（如公认的动植物物种）相关的大量数据源，这些数据源统称为生物多样性数据。然而，这些数据对人工智能提出了相当大的挑战：虽然数量迅速增长，但对于许多分类组来说，它们仍然非常不完整，经常显示来自不同来源的相互冲突的信号，并且是多模态的，因此结构不断变化。在本文中，我们激励、描述并提出了一种结合机器学习和自动推理的新型工作流程，以发现分类身份和变化的模式-即“分类智能”-从而在生物数据领域内实现可扩展且具有广泛影响力的人工智能解决方案。
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.