U.S. Department of Energy

Pacific Northwest National Laboratory

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

Publish Date: 
Monday, February 1, 2016
Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in terms of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.
Zhang B, S Choudhury, M Al-Hasan, X Ning, K Agarwal, S Purohit, and P Pesantez. 2016. "Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs." In Third Workshop on Mining Networks and Graphs: A Big Data Analytic Challenge (MNG 2016), May 7, 2016, Miami, Florida. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA.
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