This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText
The fastText algorithm was meant for word embeddings, however REF showed that a simple bag-of-words can generate surprisingly good KGEs.
3 0 O ct 2 01 7 Fast Linear Model for Knowledge Graph Embeddings