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Multi-Relational Learning with Gaussian Processes

Zhao Xu; Kristian Kersting; Volker Tresp
In: Craig Boutilier (Hrsg.). IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2009), July 11-17, Pasadena, California, USA, Pages 1309-1314, Morgan Kaufmann Publishers Inc. 2009.


Due to their flexible nonparametric nature, Gaussian process models are very effective at solving hard machine learning problems. While existing Gaussian process models focus on modeling one single relation, we present a generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest. The proposed model is analyzed in the context of bipartite, directed, and undirected univariate relations. Experimental results on real-world datasets show that exploiting the correlations among different entity types and relations can indeed improve prediction performance.

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