Statistical Relational Learning - Representation Formalisms

Representation Formalisms

One of the fundamental design goals of the representation formalisms developed in SRL is to abstract away from concrete entities and to represent instead general principles that are intended to be universally applicable. Since there are countless ways in which such principles can be represented, many representation formalisms have been proposed in recent years. In the following, some of the more common ones are listed in alphabetical order:

  • Bayesian logic programs
  • BLOG models
  • Logic programs with annotated disjunctions
  • Markov logic networks
  • Multi-entity Bayesian networks
  • Probabilistic relational models
  • Recursive random fields
  • Relational Bayesian networks
  • Relational dependency networks
  • Relational Markov networks

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