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.

人类认知的核心能力之一是类比；在面对新的情境时，我们经常将先前的经验从其他领域转化过来。多数关于计算类比的研究主要依赖于复杂的、手工制作的输入。本研究通过减少输入要求，只需要对实体进行映射，自动提取常识表示并将其用于实体之间的映射。与以往的工作不同，我们的框架能够处理部分类比并提出新的实体添加。此外，我们的方法的输出易于解释，允许用户理解为什么选择了特定的映射。实验证明，我们的模型能够正确映射 81.2% 的典型的 2x2 类比问题（猜测水平 = 50%）。在更大的问题上，它的准确率达到 77.8%（平均猜测水平 = 13.1%）。在另一个实验中，我们展示了我们的算法优于人类表现，并且自动提出的新实体与人类的建议类似。我们希望这项工作能通过为更灵活、更现实的输入要求铺平道路来推动计算类比的发展。