  Knowledge Graphs (KGs) are widely employed in artificial intelligence
applications, such as question-answering and recommendation systems. However,
KGs are frequently found to be incomplete. While much of the existing
literature focuses on predicting missing nodes for given incomplete KG triples,
there remains an opportunity to complete KGs by exploring relations between
existing nodes, a task known as relation prediction. In this study, we propose
a relations prediction model that harnesses both textual and structural
information within KGs. Our approach integrates walks-based embeddings with
language model embeddings to effectively represent nodes. We demonstrate that
our model achieves competitive results in the relation prediction task when
evaluated on a widely used dataset.
