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 dialogue agent. Making analogies requires the agent to establish 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 Internet 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 evidence. 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.

Reference

Mei Si, Craig Carlson, "A Data-Driven Approach for Making Analogies,"

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, CogSci 2017, London, UK, 16-29 July 2017.

Bibtex

@inproceedings{DBLP:conf/cogsci/SiC17,
  author    = {Mei Si and
               Craig Carlson},
  title     = {A Data-Driven Approach for Making Analogies},
  booktitle = {Proceedings of the 39th Annual Meeting of the Cognitive Science Society,
               CogSci 2017, London, UK, 16-29 July 2017},
  year      = {2017},
  crossref  = {DBLP:conf/cogsci/2017},
  url       = {https://mindmodeling.org/cogsci2017/papers/0595/index.html},
  timestamp = {Fri, 12 Jan 2018 14:51:18 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/cogsci/SiC17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}