Trust from the past: Bayesian Personalized Ranking based Link Prediction   in Knowledge Graphs
  Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different predicates and the link prediction performance of different predicates in a knowledge graph generally varies widely. In this work, we propose a latent feature embedding based link prediction model which considers the prediction task for each predicate disjointly. To learn the model parameters it utilizes a Bayesian personalized ranking based optimization technique. Experimental results on large-scale knowledge bases such as YAGO2 show that our link prediction approach achieves substantially higher performance than several state-of-art approaches. We also show that for a given predicate the topological properties of the knowledge graph induced by the given predicate edges are key indicators of the link prediction performance of that predicate in the knowledge graph. 
Baichuan Zhang, Sutanay Choudhury, Mohammad Al Hasan, Xia Ning,   Khushbu Agarwal, Sumit Purohit, Paola Pesntez Cabrera
cs.LG cs.AI cs.IR