S a C a: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025)

Abstract: 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.

## introduction knowledge graphs (kgs), which represent entities as nodes and their relationships as edges in a graph-based structure, have widespread applications in diverse fields such as knowledge discovery, question answering, recommendation systems, and clinical prediction. the applicability of kgs faces two primary challenges. firstly, real-world knowledge graphs suffer from sparsity and incompleteness despite their considerable scales. secondly, * corresponding author while most knowledge-driven applications operate in numerical spaces, knowledge graphs are predominantly represented in symbolic or logical formats. to enable efficient reasoning and knowledge acquisition, knowledge graph embedding (kge) techniques @xcite @xcite @xcite have been developed to map entities and relations into low-dimensional vector spaces, which are expected to preserve the underlying structural properties of the original graph. thus, embeddings are widely utilized in various downstream tasks, including knowledge graph completion @xcite @xcite , entity classification @xcite @xcite , and more. while current kge models have yielded promising outcomes, most still focus on explaining single triple facts (an intra-triple perspective) while overlooking the broader inter-triple relationships among triples. as a result, researchers raise questions about their ability to effectively capture structural proximity within kgs @xcite @xcite . building upon previous work, we further reveal that structural semantic drift is prevalent across existing kge models. this is evidenced by varying spearman correlation coefficients foot_0 between kg-induced similarities and cosine similarities from learned entity embeddings, as shown in fig. 1 . such variation reflects differing model abilities to preserve original kg semantics, potentially leading to suboptimal performance where fine-grained structural distinctions are critical. recent contrastive strategies @xcite @xcite attempt to address this by using standard contrastive learning to learn more discriminative entity embeddings. however, this approach suffers from three key limitations: firstly, the continuous nature of entity similarity conflicts with rigid binary labeling in simple contrastive learning, necessitating non-discrete modeling. secondly, over-reliance on individual triples for contrastive sample construction lacks mechanisms for capturing contextual consistency across graph structures. as illustrated in figs. 2 and 3 , while da vinci, raphael, and monet all share the triple (ent occupation ------→ painter), da vinci and raphael exhibit stronger semantic ties due to a greater ex-tent of contextual overlap between them. consequently, their embedding space distance should be closer than that with monet. thirdly, in real-world kgs with multiple relations, entity semantics are inherently multifaceted and vary across different relational contexts. however, existing contrastive methods often emphasize entity-level contrast, ignoring these relation-contextualized semantics. to address this, we propose structural calibration (saca), a plug-and-play framework that improves the sensitivity of a model to global structural signals in the kg. saca draws inspiration from the distributional hypothesis: entities with similar meanings tend to appear in similar structural contexts. based on this, we design four strategies to induce similarity metrics grounded in context overlap, which capture multi-scale structural cues from both entity and relation perspectives. these structural similarities are then used as soft supervision in our kg-induced soft-weighted contrastive learning (kiscl) module. unlike standard contrastive learning, kiscl introduces a continuous weighting mechanism that adjusts the loss sensitivity based on similarity confidence, enabling more nuanced and structure-aware embedding refinement. saca integrates seamlessly into existing models without adding parameters. extensive experiments show that it consistently enhances structural fidelity and improves performance across a range of kge architectures and benchmarks. ## preliminaries in this section, we briefly describe the formulation of the knowledge graph embedding (kge) and the concept of graph context within a knowledge graph (kg). a knowledge graph (kg): a general kg can be formalized as @xmath0, r, t ) where e, r are the entity set, the relation set respectively. @xmath1, r, t) | h, @xmath2, @xmath3. knowledge graph embedding. knowledge graph embedding (kge) encompasses encoding entities e and relations r into low-dimensional continuous vectors (d ≪ |e|). these embeddings are typically optimized by applying a scoring function f(h, r, t) to evaluate the plausibility of triples. ## methodology in this section, we present our framework, saca, as illustrated in fig. 4 . four combination strategies are designed to induce the structural proximity inherent in knowledge graphs, capturing both direct and indirect cues from both entity and relation perspectives. then, we propose kg-induced softweighted contrastive learning (kiscl), which leverages kg-derived similarities as continuous weighting factors in contrastive learning to refine the knowledge embedding space. finally, we seamlessly integrate our framework with existing kge methods by jointly optimizing the proposed contrastive loss and their original scoring functions, leading to a more comprehensive and effective knowledge representation.