This paper contributes a novel embedding model which measures the probability of each belief @math in a large-scale knowledge repository via simultaneously learning distributed representations for entities ( @math and @math ), relations ( @math ), and the words in relation mentions ( @math ). It facilitates knowledge completion by means of simple vector operations to discover new beliefs. Given an imperfect belief, we can not only infer the missing entities, predict the unknown relations, but also tell the plausibility of the belief, just leveraging the learnt embeddings of remaining evidences. To demonstrate the scalability and the effectiveness of our model, we conduct experiments on several large-scale repositories which contain millions of beliefs from WordNet, Freebase and NELL, and compare it with other cutting-edge approaches via competing the performances assessed by the tasks of entity inference, relation prediction and triplet classification with respective metrics. Extensive experimental results show that the proposed model outperforms the state-of-the-arts with significant improvements.
TransE @cite_11 is a canonical model different from all the other prior arts, which embeds relations into the same vector space as entities by regarding the relation @math as a translation from @math to @math , i.e. @math . It works well on beliefs with the ONE-TO-ONE mapping property but performs badly with multi-mapping beliefs. Given a series of facts associated with a ONE-TO-MANY relation @math , e.g. @math , TransE tends to represent the embeddings of entities on the MANY-side as extremely close to each other with very little discrimination.
@cite_11
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We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.