Sign In

Communications of the ACM

Letters to the Editor

What About Statistical Relational Learning?


View as: Print Mobile App ACM Digital Library In the Digital Edition Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook
Letters to the Editor, illustration

Credit: iStockPhoto.com

While Stuart Russell's review article "Unifying Logic and Probability" (July 2015) provided an excellent summary of a number of attempts to unify these two representations, it also gave an incomplete picture of the state of the art. The entire field of statistical relational learning (SRL), which was never mentioned in the article, is devoted to learning logical probabilistic models. Although the article said little is known about computationally feasible algorithms for learning the structure of these models, SRL researchers have developed a wide variety of them. Likewise, contrary to the article's statement that generic inference for logical probabilistic models remains too slow, many efficient algorithms for this purpose have been developed.

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.


 

No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.
  

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.