  We describe a deployed scalable system for organizing published scientific
literature into a heterogeneous graph to facilitate algorithmic manipulation
and discovery. The resulting literature graph consists of more than 280M nodes,
representing papers, authors, entities and various interactions between them
(e.g., authorships, citations, entity mentions). We reduce literature graph
construction into familiar NLP tasks (e.g., entity extraction and linking),
point out research challenges due to differences from standard formulations of
these tasks, and report empirical results for each task. The methods described
in this paper are used to enable semantic features in www.semanticscholar.org
