Relational Models
This is an incremental survey paper summarizing existing relational models and their applications across various domains.
This paper surveys relational models, which describe networked domains by accounting for global dependencies in data and can lead to more accurate predictions compared to non-relational machine learning approaches.
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.