KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science

arXiv:2303.02204v3 Announce Type: replace
Abstract: In recent years, we have witnessed the growing interest from academia and industry in applying data science technologies to analyze large amounts of data. In this process, a myriad of artifacts (datasets, pipeline scripts, etc.) are created. However, there has been no systematic attempt to holistically collect and exploit all the knowledge and experiences that are implicitly contained in those artifacts. Instead, data scientists recover information and expertise from colleagues or learn via trial and …

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