Probabilistic soft logic

PSL
Developer(s)LINQS Lab
Initial releaseSeptember 23, 2011 (2011-09-23)
Stable release
2.2.2[1] / May 20, 2020 (2020-05-20)
Repositorygithub.com/linqs/psl
Written inJava
PlatformLinux, macOS, Windows
TypeMachine Learning, Statistical relational learning
LicenseApache License 2.0
Websitepsl.linqs.org

Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. [2] It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment. PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge. More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model. PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state). The "softening" of the logical formulas makes inference a polynomial time operation rather than an NP-hard operation.

  1. ^ Cite error: The named reference psl:repo:2.2.2 was invoked but never defined (see the help page).
  2. ^ Cite error: The named reference bach:jmlr17 was invoked but never defined (see the help page).