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MLSea:可发现机器学习的语义层

MLSea: A Semantic Layer for Discoverable Machine Learning
课程网址: https://videolectures.net/eswc2024_dasoulas_semantic_layer/  
主讲教师: Ioannis Dasoulas
开课单位: 2024年上海世博会
开课时间: 2024-06-18
课程语种: 英语
中文简介:
随着机器学习(ML)领域的快速发展,ML管道的数量、复杂性和组件不断增长。在线平台(如OpenML、Kaggle)旨在收集和传播机器学习实验。然而,可用的知识是分散的,每个平台代表机器学习过程的不同组件或交叉组件,但方式不同。为了解决这个问题,我们利用语义网技术对机器学习数据集、实验、软件和科学作品进行建模,并将其集成到MLSea中,MLSea是一种资源,由以下部分组成:(i)MLSO,一种对机器学习数据集、管道和实现进行建模的本体;(ii)MLST,具有作为受控词汇表的ML知识集合的分类法;以及(iii)MLSea KG,一个包含ML数据集、管道、实现和来自不同来源的科学作品的RDF图。MLSea为改进ML管道的搜索、可解释性和可重复性铺平了道路。
课程简介: With the Machine Learning (ML) field rapidly evolving, ML pipelines continuously grow in numbers, complexity and components. Online platforms (e.g., OpenML, Kaggle) aim to gather and disseminate ML experiments. However, available knowledge is fragmented with each platform representing distinct components of the ML process or intersecting components but in different ways. To address this problem, we leverage semantic web technologies to model and integrate ML datasets, experiments, software and scientific works into MLSea, a resource consisting of: (i) MLSO, an ontology that models ML datasets, pipelines and implementations; (ii) MLST, taxonomies with collections of ML knowledge formulated as controlled vocabularies; and (iii) MLSea-KG, an RDF graph containing ML datasets, pipelines, implementations and scientific works from diverse sources. MLSea paves the way for improving the search, explainability and reproducibility of ML pipelines.
关 键 词: MLSea; 机器学习; 语义层
课程来源: 视频讲座网
数据采集: 2024-08-13:liyq
最后编审: 2024-08-13:liyq
阅读次数: 7