Concept relatedness estimation (CRE) aims to determine whether two given
concepts are related. Existing methods only consider the pairwise relationship
between concepts, while overlooking the higher-order relationship that could be
encoded in a concept-level graph structure. We discover that this underlying
graph satisfies a set of intrinsic properties of CRE, including reflexivity,
commutativity, and transitivity. In this paper, we formalize the CRE properties
and introduce a graph structure named ConcreteGraph. To address the data
scarcity issue in CRE, we introduce a novel data augmentation approach to
sample new concept pairs from the graph. As it is intractable for data
augmentation to fully capture the structural information of the ConcreteGraph
due to a large amount of potential concept pairs, we further introduce a novel
Graph Component Contrastive Learning framework to implicitly learn the complete
structure of the ConcreteGraph. Empirical results on three datasets show
significant improvement over the state-of-the-art model. Detailed ablation
studies demonstrate that our proposed approach can effectively capture the
high-order relationship among concepts.

本文介绍了一种名为 ConcreteGraph 的图结构，用于解决概念相关性评估中的数据稀缺问题，并结合数据增强方法和图组件对比学习方法来捕捉概念之间的高阶关系。实验证明，该方法在三个数据集上的性能优于现有方法。