  The human ability to flexibly reason using analogies with domain-general
content depends on mechanisms for identifying relations between concepts, and
for mapping concepts and their relations across analogs. Building on a recent
model of how semantic relations can be learned from non-relational word
embeddings, we present a new computational model of mapping between two
analogs. The model adopts a Bayesian framework for probabilistic graph
matching, operating on semantic relation networks constructed from distributed
representations of individual concepts and of relations between concepts.
Through comparisons of model predictions with human performance in a novel
mapping task requiring integration of multiple relations, as well as in several
classic studies, we demonstrate that the model accounts for a broad range of
phenomena involving analogical mapping by both adults and children. We also
show the potential for extending the model to deal with analog retrieval. Our
approach demonstrates that human-like analogical mapping can emerge from
comparison mechanisms applied to rich semantic representations of individual
concepts and relations.
