Title: Fast Linear Model for Knowledge Graph Embeddings,
Abstract: 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.