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.
@article{arxiv.2305.13059,
title = {Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction},
author = {Adrian Kochsiek and Apoorv Saxena and Inderjeet Nair and Rainer Gemulla},
journal= {arXiv preprint arXiv:2305.13059},
year = {2023}
}