Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be {\textquotedblleft}trained{\textquotedblright} on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google{\textquoteright}s knowledge vault project as an example of such combination.

}, issn = {0018-9219}, doi = {10.1109/JPROC.2015.2483592}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7358050}, author = {Maximilian Nickel and Kevin Murphy and Tresp, Volker and Evgeniy Gabrilovich} }