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Low-dimensional embeddings of knowledge graphs and behavior graphs haveproved remarkably powerful in varieties of tasks, from predicting unobservededges between entities to content recommendation. The two types of graphs cancontain distinct and complementary information for the same entities/nodes.However, previous works focus either on knowledge graph embedding or behaviorgraph embedding while few works consider both in a unified way. Here we presentBEM , a Bayesian framework that incorporates the information from knowledgegraphs and behavior graphs. To be more specific, BEM takes as prior thepre-trained embeddings from the knowledge graph, and integrates them with thepre-trained embeddings from the behavior graphs via a Bayesian generativemodel. BEM is able to mutually refine the embeddings from both sides whilepreserving their own topological structures. To show the superiority of ourmethod, we conduct a range of experiments on three benchmark datasets: nodeclassification, link prediction, triplet classification on two small datasetsrelated to Freebase, and item recommendation on a large-scale e-commercedataset.

Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks