Relational Models

TL;DR

This paper surveys relational models, which capture global dependencies in networked data, and discusses their advantages and applications across various domains like social networks, bioinformatics, and NLP.

Contribution

It provides a comprehensive overview of relational models, highlighting their structure, benefits, and diverse applications in real-world problems.

Findings

Relational models improve prediction accuracy over non-relational methods.

They are based on probabilistic graphical models such as Bayesian networks and Markov networks.

Applications span social networks, knowledge graphs, bioinformatics, and more.

Abstract

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.