构建一个元数据知识图,作为一个大 atlas,去神秘化AI管道优化优化
Revathy Venkataramanan1,2, Aalap Tripathy2, Tarun Kumar2
1AI Institute, University of South Carolina, Columbia, SC, United States.
Frontiers in big data
|January 22, 2025
概括
本研究介绍了AI管道元数据知识图 (AIMKG),以有效地管理和利用来自AI管道的元数据. AIMKG增强了人工智能管道搜索和建议,提高了人工智能开发的效率和可发现性.
科学领域:
- 人工智能的人工智能
- 机器学习工程 机器学习工程
- 数据科学数据科学数据科学
背景情况:
- 自动化人工智能模型培训和超参数调整是先进的,但其他管道阶段,如数据集选择和特征工程,优化程度较低.
- 提高端到端的人工智能管道效率需要从过去执行的元数据,这是计算上具有挑战性的再生.
- 现有的人工智能元数据分散在各种开源平台上,这带来了整合和统一的挑战.
研究的目的:
- 解决来自不同来源的AI管道元数据集成和统一的挑战.
- 为提高效率引入用于采购和管理AI管道元数据的解决方案.
- 为人工智能管道元数据构建一个全面的知识图,以帮助搜索和推.
主要方法:
- 来源AI管道元数据来自开源平台,如Papers-with-Code,OpenML和拥抱脸.
- 引入了共同元数据本体 (CMO) 以统一不同的术语和数据格式.
- 构建了一个广泛的AI管道元数据知识图 (AIMKG),包含160万条管道,并应用了语义增强.
主要成果:
- 构建了人工智能管道元数据知识图 (AIMKG),包含160万条管道.
- 定量评估显示,一个定制的聚合模型实现了76.3%的检索精度 (R@1),表现优于基线.
- 定性分析表明,基于AIMKG的建议在78%的案例中是相关的,超过了基于MLSchema的推者 (51%).
结论:
- AIMKG 作为一个有价值的资源,用于导航 AI 景观,为从业者提供AI管道优化见解.
- 知识图方便数据挖掘和分析不断发展的AI工作流程.
- AIMKG显著改善了相关AI管道的搜索和推,提高了整体AI开发效率.
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