Knowledge Graph Embedding (KGE) seeks to learn latent representations of entities and relations to support knowledge-driven AI systems. However, existing KGE approaches often exhibit a growing discrepancy between the learned embedding space and the intrinsic structural semantics of the underlying knowledge graph. This divergence primarily stems from the over-reliance on geometric criteria for assessing triple plausibility, whose effectiveness is inherently limited by the sparsity of factual triples and the disregard of higher-order structural dependencies in the knowledge graph. To overcome this limitation, we introduce Structure-aware Calibration (SaCa), a versatile framework designed to calibrate KGEs through the integration of global structural patterns. SaCa designs two new components: (i) Structural Proximity Measurement, which captures multi-order structural signals from both entity and entity-relation perspectives; and (ii) KG-Induced Soft-weighted Contrastive Learning (KISCL), which assigns soft weights to hard-to-distinguish positive and negative pairs, enabling the model to better reflect nuanced structural dependencies. Extensive experiments on seven benchmarks demonstrate that SaCa consistently boosts performance across ten KGE models on link prediction and entity classification tasks with minimal overhead.