A Data-Driven Approach for Making Analogies.
Mei Si, Craig Carlson · eScholarship (California Digital Library) · 2017
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.