Mapping And Revising Markov Logic Networks For Transfer Learning

Abstract

Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This paper considers transfer learning with Markov logic networks (MLNs), a powerful formalism for learning in relational domains. We present a complete MLN transfer system that first autonomously maps the predicates in the source MLN to the target domain and then revises the mapped structure to further improve its accuracy. Our results in several real-world domains demonstrate that our approach successfully reduces the amount of time and training data needed to learn an accurate model of a target domain over learning from scratch.