Knowledge Graph Completion Model Based on Semantic Constrained Hybrid Scoring

Abstract

Knowledge Graph Completion (KGC) aims to predict missing links in knowledge bases. However, existing methods often struggle with reasoning over complex semantic relations and generate predictions that violate common-sense logic. In this paper, a knowledge graph completion model based on semantic constrained hybrid scoring (SCHS) is proposed, which integrates three synergistic mechanisms. First, an Adaptive Feature Gating (AFG) mechanism is designed for feature recalibration. Unlike static embeddings, this module dynamically recalibrates the importance of different semantic aspects based on the relation context. Second, a Relation-Concept Matrix Projection (RCMP) mechanism is introduced to act as an explicit semantic constraint. This mechanism maps triplets to a latent concept space to capture the inherent consistency between relations and concepts, providing a penalization mechanism to filter out noise that is topologically close but semantically invalid. Third, an Adaptive Fusion Strategy (AFS) utilizing a soft-start mechanism is introduced to effectively balance structural and semantic scores without early-training collapses. This tri-fold synergy essentially resolves the rigid trade-off between geometric alignment and flexible semantic matching. Extensive experiments on the WN18RR and FB15k-237 datasets demonstrate that SCHS achieves substantial performance improvements, verifying the robustness and effectiveness of the dynamically regulated fusion of semantics and structures in the alleviation of long-tail errors.