  Knowledge graph embedding (KGE) models represent each entity and relation of
a knowledge graph (KG) with low-dimensional embedding vectors. These methods
have recently been applied to KG link prediction and question answering over
incomplete KGs (KGQA). KGEs typically create an embedding for each entity in
the graph, which results in large model sizes on real-world graphs with
millions of entities. For downstream tasks these atomic entity representations
often need to be integrated into a multi stage pipeline, limiting their
utility. We show that an off-the-shelf encoder-decoder Transformer model can
serve as a scalable and versatile KGE model obtaining state-of-the-art results
for KG link prediction and incomplete KG question answering. We achieve this by
posing KG link prediction as a sequence-to-sequence task and exchange the
triple scoring approach taken by prior KGE methods with autoregressive
decoding. Such a simple but powerful method reduces the model size up to 98%
compared to conventional KGE models while keeping inference time tractable.
After finetuning this model on the task of KGQA over incomplete KGs, our
approach outperforms baselines on multiple large-scale datasets without
extensive hyperparameter tuning.
