SynergyKGC: Reconciling Topological Heterogeneity in Knowledge Graph Completion via Topology-Aware Synergy

2026-02-11

AI Summary

This work addresses the “structural resolution mismatch” problem in knowledge graph completion—where structural noise in dense regions and representation collapse in sparse regions degrade performance—by proposing a topology-aware collaborative learning framework. The approach innovatively integrates relation-aware cross-attention, semantic-intent-driven gating, density-dependent identity anchoring, and a dual-tower consistency architecture to enable adaptive fusion and stable representation of heterogeneous topological structures. Experimental results on two public benchmarks demonstrate that the method significantly improves hit rates and effectively mitigates representation imbalance caused by structural heterogeneity, thereby validating its robustness in fusing information across non-uniform graph data.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Knowledge Graph Completion
• Topological Heterogeneity
• Structural Resolution Mismatch
• Relational Reasoning
• Graph Density

Innovation

• Cross-Modal Synergy
• Topology-Aware
• Identity Anchoring
• Double-tower Consistency
• Structural Resolution Mismatch