  We provide a survey on relational models. Relational models describe complete
networked {domains by taking into account global dependencies in the data}.
Relational models can lead to more accurate predictions if compared to
non-relational machine learning approaches. Relational models typically are
based on probabilistic graphical models, e.g., Bayesian networks, Markov
networks, or latent variable models. Relational models have applications in
social networks analysis, the modeling of knowledge graphs, bioinformatics,
recommendation systems, natural language processing, medical decision support,
and linked data.
