  Scholarly Knowledge Graphs (KGs) provide a rich source of structured
information representing knowledge encoded in scientific publications. With the
sheer volume of published scientific literature comprising a plethora of
inhomogeneous entities and relations to describe scientific concepts, these KGs
are inherently incomplete. We present exBERT, a method for leveraging
pre-trained transformer language models to perform scholarly knowledge graph
completion. We model triples of a knowledge graph as text and perform triple
classification (i.e., belongs to KG or not). The evaluation shows that exBERT
outperforms other baselines on three scholarly KG completion datasets in the
tasks of triple classification, link prediction, and relation prediction.
Furthermore, we present two scholarly datasets as resources for the research
community, collected from public KGs and online resources.
