Analogical reasoning is the process of discovering and mapping
correspondences from a target subject to a base subject. As the most well-known
computational method of analogical reasoning, Structure-Mapping Theory (SMT)
abstracts both target and base subjects into relational graphs and forms the
cognitive process of analogical reasoning by finding a corresponding subgraph
(i.e., correspondence) in the target graph that is aligned with the base graph.
However, incorporating deep learning for SMT is still under-explored due to
several obstacles: 1) the combinatorial complexity of searching for the
correspondence in the target graph; 2) the correspondence mining is restricted
by various cognitive theory-driven constraints. To address both challenges, we
propose a novel framework for Analogical Reasoning (DeepGAR) that identifies
the correspondence between source and target domains by assuring cognitive
theory-driven constraints. Specifically, we design a geometric constraint
embedding space to induce subgraph relation from node embeddings for efficient
subgraph search. Furthermore, we develop novel learning and optimization
strategies that could end-to-end identify correspondences that are strictly
consistent with constraints driven by the cognitive theory. Extensive
experiments are conducted on synthetic and real-world datasets to demonstrate
the effectiveness of the proposed DeepGAR over existing methods.

本文提出的基于深度学习的类比推理框架 DeepGAR 可以在满足认知理论驱动的约束条件下，通过设计几何约束嵌入空间诱导节点嵌入的子图关系以进行有效的子图搜索，并通过开发新的学习和优化策略来端到端地识别与认知理论驱动的约束条件严格一致的对应物，从而超越现有的方法。

DeepGAR: 深度图学习用于类比推理

DeepGAR: Deep Graph Learning for Analogical Reasoning

Analogy is core to human cognition. It allows us to solve problems based on
prior experience, it governs the way we conceptualize new information, and it
even influences our visual perception. The importance of analogy to humans has
made it an active area of research in the broader field of artificial
intelligence, resulting in data-efficient models that learn and reason in
human-like ways. While cognitive perspectives of analogy and deep learning have
generally been studied independently of one another, the integration of the two
lines of research is a promising step towards more robust and efficient
learning techniques. As part of a growing body of research on such an
integration, we introduce the Analogical Matching Network: a neural
architecture that learns to produce analogies between structured, symbolic
representations that are largely consistent with the principles of
Structure-Mapping Theory.

该研究介绍了一种基于结构映射理论的神经网络结构 —— 模拟匹配网络（Analogical Matching Network），通过融合类比与深度学习，产生了一种人工智能模型，能够有效地学习和推理，解决新问题。