  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 error. Hence, this paper
presents a scalable platform, KGLiDS, that employs machine learning and
knowledge graph technologies to abstract and capture the semantics of data
science artifacts and their connections. Based on this information, KGLiDS
enables various downstream applications, such as data discovery and pipeline
automation. Our comprehensive evaluation covers use cases in data discovery,
data cleaning, transformation, and AutoML. It shows that KGLiDS is
significantly faster with a lower memory footprint than the state-of-the-art
systems while achieving comparable or better accuracy.
