FAME: Flexible, Scalable Analogy Mappings Engine
Shahar Jacob Chen Shani Dafna Shahaf
Analogy is one of the core capacities of human cognition; when faced with new
situations, we often transfer prior experience from other domains. Most work on
computational analogy relies heavily on complex, manually crafted input. In
this work, we relax the input requirements, requiring only names of entities to
be mapped. We automatically extract commonsense representations and use them to
identify a mapping between the entities. Unlike previous works, our framework
can handle partial analogies and suggest new entities to be added. Moreover,
our method's output is easily interpretable, allowing for users to understand
why a specific mapping was chosen.
  Experiments show that our model correctly maps 81.2% of classical 2x2 analogy
problems (guess level=50%). On larger problems, it achieves 77.8% accuracy
(mean guess level=13.1%). In another experiment, we show our algorithm
outperforms human performance, and the automatic suggestions of new entities
resemble those suggested by humans. We hope this work will advance
computational analogy by paving the way to more flexible, realistic input
requirements, with broader applicability.