Mapping and Revising Markov Logic Networks for Transfer Learning
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 withMarkov 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.