Markov Logic Network Family

A Markov Logic Network Family is a logic family that is expressed with Markov logic.

• AKA: MLNs, Markov Logic Networks.
• Context:
  • It can be supported by a Markov Logic Inference Algorithm.
  • It can be supported by a Markov Logic Learning Algorithm.
  • …
• Counter-Example(s):
  • Conditional Random Fields.
  • Factor Graph Models.
  • Probabilistic Logic Programming.
• See: First-Order Logic.

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References

2015

• (Domingos et al., 2015) ⇒ Pedro Domingos, Kristian Kersting, Raymond Mooney, and Jude Shavlik. (2015). “What About Statistical Relational Learning?.” In: Communications of the ACM Journal, 58(12). doi:10.1145/2841423
  • QUOTE:The article mentioned Markov logic networks (MLNs), arguably the leading approach to unifying logic and probability, but did not accurately describe them. While the article conflated MLNs with Nilsson's probabilistic logic, the two are quite different in a number of crucial respects. For Nilsson, logical formulas are indivisible constraints; in contrast, MLNs are log-linear models that use first-order formulas as feature templates, with one feature per grounding of the formula. This novel use of first-order formulas allows MLNs to compactly represent most graphical models, something previous probabilistic logics could not do. This capability contributes significantly to the popularity of MLNs. And since MLNs subsume first-order Bayesian networks, the article's claim that MLNs have problems with variable numbers of objects and irrelevant objects that Bayes-net approaches avoid is incorrect. MLNs and their variants cannot only handle object uncertainty but relation uncertainty as well. Further, the article said MLNs perform inference by applying MCMC to a ground network, but several lifted inference algorithms for them exist.

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