Construction of the Literature Graph in Semantic Scholar

Abstract

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

arXiv commentTo appear in NAACL 2018 industry track

References in corpus (2)

• Frustratingly Easy Domain Adaptation
• Content-Based Citation Recommendation

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