  The ability of knowledge graphs to represent complex relationships at scale
has led to their adoption for various needs including knowledge representation,
question-answering, and recommendation systems. Knowledge graphs are often
incomplete in the information they represent, necessitating the need for
knowledge graph completion tasks. Pre-trained and fine-tuned language models
have shown promise in these tasks although these models ignore the intrinsic
information encoded in the knowledge graph, namely the entity and relation
types. In this work, we propose the Knowledge Graph Language Model (KGLM)
architecture, where we introduce a new entity/relation embedding layer that
learns to differentiate distinctive entity and relation types, therefore
allowing the model to learn the structure of the knowledge graph. In this work,
we show that further pre-training the language models with this additional
embedding layer using the triples extracted from the knowledge graph, followed
by the standard fine-tuning phase sets a new state-of-the-art performance for
the link prediction task on the benchmark datasets.
