  Knowledge graph embedding techniques are widely used for knowledge graph
refinement tasks such as graph completion and triple classification. These
techniques aim at embedding the entities and relations of a Knowledge Graph
(KG) in a low dimensional continuous feature space. This paper adopts a
transformer-based triplet network creating an embedding space that clusters the
information about an entity or relation in the KG. It creates textual sequences
from facts and fine-tunes a triplet network of pre-trained transformer-based
language models. It adheres to an evaluation paradigm that relies on an
efficient spatial semantic search technique. We show that this evaluation
protocol is more adapted to a few-shot setting for the relation prediction
task. Our proposed GilBERT method is evaluated on triplet classification and
relation prediction tasks on multiple well-known benchmark knowledge graphs
such as FB13, WN11, and FB15K. We show that GilBERT achieves better or
comparable results to the state-of-the-art performance on these two refinement
tasks.
