Paper Info
Title
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Abstract
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous works focus either on knowledge graph embedding or behavior graph embedding while few works consider both in a unified way. Here we present BEM, a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs. To be more specific, BEM takes as prior the pre-trained embeddings from the knowledge graph, and integrates them with the pre-trained embeddings from the behavior graphs via a Bayesian generative model. BEM is able to mutually refine the embeddings from both sides while preserving their own topological structures. To show the superiority of our method, we conduct a range of experiments on three benchmark datasets: node classification, link prediction, triplet classification on two small datasets related to Freebase, and item recommendation on a large-scale e-commerce dataset.
Year
DOI
Venue
2019
10.1145/3357384.3358014
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
bayesian model, graph embedding, knowledge graph
Data mining,Knowledge graph,Embedding,Computer science,Theoretical computer science,Refining (metallurgy),Bayes' theorem
Conference
ISBN
Citations 
PageRank 
978-1-4503-6976-3
2
0.35
References 
Authors
0
7
Name
Order
Citations
PageRank
Yuting Ye131.37
Xuwu Wang220.35
Jiangchao Yao3164.98
Kunyang Jia430.70
Jingren Zhou576775.63
Yanghua Xiao648254.90
Hongxia Yang727135.55