Expeditious Generation of Knowledge Graph Embeddings

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. In this paper, we propose KG2VEC, a novel approach to Knowledge Graph Embedding based on the skip-gram model. Instead of using a predefined scoring function, we learn it relying on Long ShortTerm Memories. We evaluated the goodness of our embeddings on knowledge graph completion and show that KG2VEC is comparable to the quality of the scalable state-of-the-art approach RDF2Vec and can process large graphs by parsing more than a hundred million triples in less than 6 hours on common hardware.