FAME: Flexible, Scalable Analogy Mappings Engine
This work advances computational analogy by enabling more flexible and realistic input, potentially benefiting AI systems that rely on analogical reasoning.
The paper tackled the problem of computational analogy by relaxing input requirements to only entity names, automatically extracting commonsense representations to identify mappings, and achieved 81.2% accuracy on classical 2x2 analogy problems and 77.8% on larger ones.
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.