In recent years, we saw the emergence of several approaches for producing machine-readable, semantically rich, interlinked description of the content of research publications, typically encoded as knowledge graphs. A common limitation of these solutions is that they address a low number of articles, either because they rely on human experts to summarize information from the literature or because they focus on specific research areas. In this paper, we introduce the Computer Science Knowledge Graph (CS-KG), a large-scale knowledge graph composed by over 350M RDF triples describing 41M statements from 6.7M articles about 10M entities linked by 179 semantic relations. It was automatically generated and will be periodically updated by applying an information extraction pipeline on a large repository of research papers. CS-KG is much larger than all comparable solutions and offers a very comprehensive representation of tasks, methods, materials, and metrics in Computer Science. It can support a variety of intelligent services, such as advanced literature search, document classification, article recommendation, trend forecasting, hypothesis generation, and many others. CS-KG was evaluated against a benchmark of manually annotated statements, yielding excellent results.