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Link prediction, or predicting the likelihood of a link in a knowledge graphbased on its existing state is a key research task. It differs from atraditional link prediction task in that the links in a knowledge graph arecategorized into different predicates and the link prediction performance ofdifferent predicates in a knowledge graph generally varies widely. In thiswork, we propose a latent feature embedding based link prediction model whichconsiders the prediction task for each predicate disjointly. To learn the modelparameters it utilizes a Bayesian personalized ranking based optimizationtechnique. Experimental results on large-scale knowledge bases such as YAGO2show that our link prediction approach achieves substantially higherperformance than several state-of-art approaches. We also show that for a givenpredicate the topological properties of the knowledge graph induced by thegiven predicate edges are key indicators of the link prediction performance ofthat predicate in the knowledge graph.

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs