KGLiDS is a platform for constructing a knowledge graph for linked data science. We employ machine learning to extract the semantics of data science pipelines and capture them in a knowledge graph, which can then be exploited to assist data scientists in various ways. This abstraction is the key to enabling Linked Data Science since it allows us to share the essence of pipelines between platforms, companies, and institutions without revealing critical internal information. Instead, it focuses on the semantics of what is being processed and how. We are developing different applications on top of our linked data science (LiDS) graph to automate various aspects of data science pipelines. Examples of these applications are KGpip and KGFram.