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.
@article{arxiv.1803.07828,
title = {Expeditious Generation of Knowledge Graph Embeddings},
author = {Tommaso Soru and Stefano Ruberto and Diego Moussallem and André Valdestilhas and Alexander Bigerl and Edgard Marx and Diego Esteves},
journal= {arXiv preprint arXiv:1803.07828},
year = {2018}
}
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Submitted to the Archives of Data Science, Series A; 14 pages