Human reasoning goes beyond knowledge about individual entities, extending to inferences based on relations between entities. Here we focus on the use of relations in verbal analogical mapping, sketching a general approach based on assessing similarity between patterns of semantic relations between words. This approach combines research in artificial intelligence with work in psychology and cognitive science, with the aim of minimizing hand coding of text inputs for reasoning tasks. The computational framework takes as inputs vector representations of individual word meanings, coupled with semantic representations of the relations between words, and uses these inputs to form semantic-relation networks for individual analogues. Analogical mapping is operationalized as graph matching under cognitive and computational constraints. The approach highlights the central role of semantics in analogical mapping.

人类推理超越了关于个体实体的知识，扩展到基于关系的推理实体之间。在这里，我们专注于在语言类比映射中关系的使用，勾勒出一种基于评估单词之间语义关系模式之间相似性的一般方法。这种方法将人工智能的研究与心理学和认知科学的工作相结合，旨在最大限度地减少用于推理任务的文本输入的手动编码。计算框架将单个单词含义的向量表示作为输入，再加上单词之间关系的语义表示，并使用这些输入为单个类似物形成语义关系网络。类比映射可操作为在认知和计算约束下的图匹配。该方法突出了语义在类比映射中的核心作用。