全球环境议程迫切需要一个知识的语义网络
Stefano Balbi1,2, Kenneth J Bagstad3, Ainhoa Magrach4,5
1Basque Centre for Climate Change (BC3), Scientific Campus of the University of the Basque Country, Sede Building 1, 1st floor, Barrio Sarriena S/N, 48940, Leioa, Bizkaia, Spain. stefano.balbi@bc3research.org.
Environmental evidence
|September 18, 2024
概括
人工智能 (AI) 可以帮助合成全球环境挑战的科学证据. 通过使信息具有机器可读性,人工智能加速了气候变化和生物多样性保护工作的知识整合.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
背景情况:
- 气候变化和生物多样性丧失等全球环境挑战需要综合科学证据.
- 当前的科学信息在各个尺度和学科之间分散,阻碍了有效的合成.
- 大量的科学数据压倒了传统的基于人类的合成方法.
研究的目的:
- 探索人工智能 (AI) 如何改善对环境挑战的科学证据的整合和合成.
- 突出语义技术和机器推理在解决知识差距方面的潜力.
- 倡导在开放科学基础设施中采用人工智能和共享标准.
主要方法:
- 利用语义技术和机器推理,使科学信息能够被机器阅读.
- 开发机器搜索,组织,重复使用,组合和合成信息的方法.
- 利用开放科学基础设施,包括公共数据和模型存储库.
主要成果:
- 基于人工智能的方法可以显著提高科学信息合成的速度和新性.
- 机器可读数据和共同标准对于建立集体知识库至关重要.
- 语义标签可以有效地检索和整合各种来源的信息.
结论:
- 人工智能,特别是语义和机器推理技术,为环境科学中的证据合成挑战提供了强大的解决方案.
- 科学家和政策制定者更广泛地采用和理解这些人工智能技术是必不可少的.
- 在开放科学基础设施中实施共享语义和标准将促进协作知识的建立和决策的应用.
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