Knowledge Graph Completion (KGC) fundamentally hinges on the coherent fusion of pre-trained entity semantics with heterogeneous topological structures to facilitate robust relational reasoning. However, existing paradigms encounter a critical "structural resolution mismatch," failing to reconcile divergent representational demands across varying graph densities, which precipitates structural noise interference in dense clusters and catastrophic representation collapse in sparse regions. We present SynergyKGC, an adaptive framework that advances traditional neighbor aggregation to an active Cross-Modal Synergy Expert via relation-aware cross-attention and semantic-intent-driven gating. By coupling a density-dependent Identity Anchoring strategy with a Double-tower Coherent Consistency architecture, SynergyKGC effectively reconciles topological heterogeneity while ensuring representational stability across training and inference phases. Systematic evaluations on two public benchmarks validate the superiority of our method in significantly boosting KGC hit rates, providing empirical evidence for a generalized principle of resilient information integration in non-homogeneous structured data.

翻译：知识图谱补全（KGC）从根本上依赖于将预训练的实体语义与异质的拓扑结构进行连贯融合，以促进稳健的关系推理。然而，现有范式面临一个关键的“结构分辨率失配”问题，无法调和不同图密度下相异的表征需求，这导致在稠密簇中产生结构噪声干扰，并在稀疏区域引发灾难性的表征坍缩。我们提出了SynergyKGC，一个自适应框架，它通过关系感知的交叉注意力和语义意图驱动的门控机制，将传统的邻居聚合提升为一种主动的跨模态协同专家。通过将密度依赖的身份锚定策略与双塔连贯一致性架构相结合，SynergyKGC有效地调和了拓扑异质性，同时确保了训练和推理阶段表征的稳定性。在两个公开基准上的系统评估验证了我们的方法在显著提升KGC命中率方面的优越性，为非均匀结构化数据中弹性信息整合的通用原理提供了实证依据。