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.

翻译：类比是人类认知的核心能力之一；面对新情境时，我们常从其他领域迁移先验经验。现有计算类比研究大多依赖复杂的人工构建输入。本文放宽了输入要求，仅需提供待映射的实体名称。我们自动抽取常识表征，并利用其识别实体间的映射关系。与先前工作不同，本框架能处理部分类比并建议新增实体。此外，方法输出具有高度可解释性，便于用户理解特定映射的选取依据。实验表明，在经典2×2类比问题中（猜中基线=50%），模型正确映射率达81.2%；在更复杂问题上达到77.8%准确率（平均猜中基线=13.1%）。另一实验显示，本算法超越人类表现，且自动推荐的新增实体与人类推荐结果高度相似。我们期望该工作通过降低对输入条件的依赖、增强泛化能力，为计算类比研究开拓更灵活、更贴近实际的输入范式。