Improving the Accuracy and Efficiency of MAP Inference for Markov Logic

TL;DR

This paper introduces Cutting Plane Inference (CPI), a meta algorithm for MAP inference in Markov Logic that enhances speed and accuracy by decomposing complex networks and integrating with existing inference methods.

Contribution

The paper proposes CPI, a novel meta algorithm that improves the efficiency and accuracy of MAP inference in Markov Logic by combining small network instantiations with traditional methods.

Findings

CPI significantly speeds up MAP inference methods.

CPI improves MaxWalkSAT accuracy.

CPI maintains the exactness of Integer Linear Programming.

Abstract

In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta algorithm that instantiates small parts of a large and complex Markov Network and then solves these using a conventional MAP method. We evaluate CPI on two tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in two different MAP inference methods: the current method of choice for MAP inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We observe that when used with CPI both methods are significantly faster than when used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains the exactness of Integer Linear Programming.