Knowledge graphs completion via probabilistic reasoning

Abstract

Constructing large-scale knowledge base has encountered a bottleneck because of the limitation of natural language processing. Many approaches have been put forward to infer new facts based on existing knowledge. Graph feature models mine rule-like patterns from a knowledge base and use them to predict missing edges. These models take account of the graph structure information and they can explain the existence of a fact reasonably. Existing models only describe local interaction between entities, but how to model co-relationships among facts globally is a tough problem. In this paper, we develop an efficient model which uses association rules to make inferences. First, we use a rule mining model to detect simple association rules and use them to produce large amounts of evidence. Second, based on all the produced evidence and the connections among them, we construct a factor graph which represents the inference space. Then, we develop an EM inference model, wherein the E-step we use Belief Propagation to calculate the marginal distribution of candidate edges and, in the M-step we propose a Generalized Iterative Proportional Fitting algorithm to re-learn the confidence of soft rules. Experiments show that our approach outperforms state-of-the-art approaches in knowledge base completion (KBC) tasks.