  Knowledge Graph Embedding methods aim at representing entities and relations
in a knowledge base as points or vectors in a continuous vector space. Several
approaches using embeddings have shown promising results on tasks such as link
prediction, entity recommendation, question answering, and triplet
classification. However, only a few methods can compute low-dimensional
embeddings of very large knowledge bases without needing state-of-the-art
computational resources. In this paper, we propose KG2Vec, a simple and fast
approach to Knowledge Graph Embedding based on the skip-gram model. Instead of
using a predefined scoring function, we learn it relying on Long Short-Term
Memories. We show that our embeddings achieve results comparable with the most
scalable approaches on knowledge graph completion as well as on a new metric.
Yet, KG2Vec can embed large graphs in lesser time by processing more than 250
million triples in less than 7 hours on common hardware.
