DeepGAR: Deep Graph Learning for Analogical Reasoning
- URL: http://arxiv.org/abs/2211.10821v1
- Date: Sat, 19 Nov 2022 23:12:58 GMT
- Title: DeepGAR: Deep Graph Learning for Analogical Reasoning
- Authors: Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang,
Haifeng Chen, and Liang Zhao
- Abstract summary: Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject.
Structure-Mapping Theory (SMT) abstracts both target and base subjects into graphs and forms the cognitive process of analogical reasoning.
We propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints.
- Score: 31.679051203515655
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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