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Expeditious Generation of Knowledge Graph Embeddings
[article]
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
arXiv:1803.07828v2
fatcat:u5gumfjwlfckjjrn7vzidy73py