The human ability to flexibly reason using analogies with domain-general content depends on mechanisms for identifying relations between concepts, and for mapping concepts and their relations across analogs. Building on a recent model of how semantic relations can be learned from nonrelational word embeddings, we present a new computational model of mapping between two analogs. The model adopts a Bayesian framework for probabilistic graph matching, operating on semantic relation networks constructed from distributed representations of individual concepts and of relations between concepts. Through comparisons of model predictions with human performance in a novel mapping task requiring integration of multiple relations, as well as in several classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children. We also show the potential for extending the model to deal with analog retrieval. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations.

中文翻译：

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具有语义关系网络的概率类比映射。

人类使用与领域一般内容的类比来灵活推理的能力取决于识别概念之间关系的机制，以及映射概念及其跨类比关系的机制。基于如何从非关系词嵌入中学习语义关系的最新模型，我们提出了两个类似物之间映射的新计算模型。该模型采用贝叶斯框架进行概率图匹配，在由单个概念和概念之间的关系的分布式表示构建的语义关系网络上运行。通过在需要整合多种关系的新型映射任务以及几项经典研究中将模型预测与人类表现进行比较，我们证明该模型解释了广泛的现象，涉及成人和儿童的类比映射。我们还展示了扩展模型以处理模拟检索的潜力。我们的方法表明，类似人类的类比映射可以从应用于个体概念和关系的丰富语义表示的比较机制中产生。