  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.
