Scaling-Up Inference in Markov Logic

Markov Logic is a powerful representation that unifies first-order logic and probabilistic graphical models. However, scaling-up inference in Markov Logic Networks (MLNs) is extremely challenging. Standard graphical model inference algorithms operate on the propositional Markov network obtained by grounding the MLN and do not scale well as the number of objects in the realworld domain increases. On the other hand, algorithms which perform inference directly at the first-order level, namely lifted inference algorithms, although more scalable than propositional algorithms, require the MLN to have specific symmetric structure. Worse still, evidence breaks symmetries, and the performance of lifted inference is the same as propositional inference (or sometimes worse, due to overhead). In this paper, we propose a general method for solving this “evidence” problem. The main idea in our method is to approximate the given MLN having, say, n objects by an MLN having k objects such that k...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Deepak Venugopal
• Keywords: AAAI 2015, Intelligent Agents