解决知识图谱补全中的结构异质性问题，提升稀疏与密集区域的表示稳定性。
SynergyKGC: Reconciling Topological Heterogeneity in Knowledge Graph Completion via Topology-Aware Synergy
- 通过关系感知交叉注意力和语义意图门控实现跨模态协同融合
- 在两个公开数据集上显著提升命中率，稀疏区域表现更优
- 适合关注复杂结构数据建模与鲁棒推理的研究者
知识图谱补全（KGC）的核心在于将预训练实体语义与异构拓扑结构协同融合，以支持稳健的关系推理。然而，现有方法存在‘结构分辨率不匹配’问题，无法调和不同图密度下的表征需求，导致稠密区域受结构噪声干扰，稀疏区域出现表征崩溃。我们提出SynergyKGC，一种自适应框架，将传统邻居聚合升级为关系感知的跨模态协同专家，结合密度依赖的身份锚定策略与双塔一致性架构，在训练与推理阶段均有效缓解拓扑异质性，保障表征稳定性。在两个公开基准上的系统评估表明，该方法显著提升KGC命中率，验证了非均匀结构数据中鲁棒信息融合的通用原则。
原文摘要 · Abstract (English)
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.
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