Expeditious Generation of Knowledge Graph Embeddings [article]

Tommaso Soru, Stefano Ruberto, Diego Moussallem, André Valdestilhas, Alexander Bigerl, Edgard Marx, Diego Esteves
2018 arXiv   pre-print
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
more » ... G2Vec, 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.
arXiv:1803.07828v2 fatcat:u5gumfjwlfckjjrn7vzidy73py