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 Short-Term 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 approaches and can process large graphs by parsing more than a hundred million triples in less than 6 hours on common hardware.

翻译：知识嵌入图方法旨在代表一个知识库中的实体和关系,作为连续矢量空间中的点或矢量。使用嵌入的几种方法在诸如链接预测、实体建议、问答和三重分类等任务上显示了有希望的结果。然而,只有几种方法可以计算非常大的知识库的低维嵌入。在本文中,我们提议KG2Vec,这是基于跳格模型的一种新颖的知识图嵌入方法。我们不使用预先定义的评分功能,而是依靠长期短期记忆来学习它。我们评估了我们嵌入知识图完成过程的优异性,并表明KG2Vec与可缩放的最先进方法的质量相当,并且可以通过在6小时内对通用硬件进行超过1亿个三倍的分解处理大图。