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A Data Driven Approach for Making Analogies
Making analogies is an important way for people to explain
and understand new concepts. Though making analogies is
natural for human beings, it is not a trivial task for a dia-
logue agent. Making analogies requires the agent to estab-
lish a correspondence between concepts in two different
domains. In this work, we explore a data-driven approach
for making analogies automatically. Our proposed approach
works with data represented as a flat graphical structure,
which can either be designed manually or extracted from In-
ternet data. For a given concept from the base domain, our
analogy agent can automatically suggest a corresponding
concept from the target domain, and a set of mappings be-
tween the relationships each concept has as supporting evi-
dence. We demonstrate the working of this algorithm by
both reproducing a classical example of analogy inference
and making analogies in new domains generated from
DBPedia data