  Sparsity of formal knowledge and roughness of non-ontological construction
make sparsity problem particularly prominent in Open Knowledge Graphs
(OpenKGs). Due to sparse links, learning effective representation for few-shot
entities becomes difficult. We hypothesize that by introducing negative
samples, a contrastive learning (CL) formulation could be beneficial in such
scenarios. However, existing CL methods model KG triplets as binary objects of
entities ignoring the relation-guided ternary propagation patterns and they are
too generic, i.e., they ignore zero-shot, few-shot and synonymity problems that
appear in OpenKGs. To address this, we propose TernaryCL, a CL framework based
on ternary propagation patterns among head, relation and tail. TernaryCL
designs Contrastive Entity and Contrastive Relation to mine ternary
discriminative features with both negative entities and relations, introduces
Contrastive Self to help zero- and few-shot entities learn discriminative
features, Contrastive Synonym to model synonymous entities, and Contrastive
Fusion to aggregate graph features from multiple paths. Extensive experiments
on benchmarks demonstrate the superiority of TernaryCL over state-of-the-art
models.
