The task of knowledge graph completion (KGC) is to infer missing links that exist between entities in knowledge graphs (KGs). However, real-world KGs are typically structurally sparse, resulting in insufficient neighborhood context and thus degraded KGC performance. Meanwhile, most existing methods adopt coarse-grained relation modeling and overlook meaningful semantic representations of relations. To tackle these issues, we propose relation-aware context aggregation (RACA) for sparse KGC. Specifically, we introduce a dual-mode relational learning mechanism that jointly learns from both within-relation and across-relation perspectives, capturing task-relevant dimensional semantics within each relation and implicit logical regularities across relations. A spatial gating unit is further designed to integrate these relational signals and generate expressive relation representations. Finally, we develop a dynamic relation attention network that leverages relation-aware features to dynamically guide contextual aggregation, enabling more effective context capture under sparsity. Extensive experiments on three benchmark datasets show that RACA achieves superior performance over state-of-the-art baselines, particularly in sparse scenarios.
Guan et al. (Wed,) studied this question.