  We propose KGT5-context, a simple sequence-to-sequence model for link
prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP
model that exploits textual features of the KG, has small model size, and is
scalable. To reach good predictive performance, however, KGT5 relies on an
ensemble with a knowledge graph embedding model, which itself is excessively
large and costly to use. In this short paper, we show empirically that adding
contextual information - i.e., information about the direct neighborhood of the
query entity - alleviates the need for a separate KGE model to obtain good
performance. The resulting KGT5-context model is simple, reduces model size
significantly, and obtains state-of-the-art performance in our experimental
study.
