Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion

2025-06-29

AI Summary

Existing knowledge graph completion (KGC) methods predominantly rely on static embedding scoring functions, limiting their ability to capture contextual dependencies and temporal evolution of relations. To address this, we propose Flow-Modulated Scoring—a novel framework that introduces conditional flow matching to KGC for the first time. Our approach models fine-grained, context-aware embedding transformation paths from head to tail entities, explicitly encoding dynamic relational semantics. It integrates a semantic context learning module with a conditional vector field learned via flow matching, enabling dynamic score calibration. Extensive experiments on standard benchmarks—including FB15k-237 and WN18RR—demonstrate substantial improvements over state-of-the-art methods, validating the framework’s superiority in modeling complex semantic relationships and enhancing prediction accuracy.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Effective modeling of multifaceted relations is pivotal for Knowledge Graph Completion (KGC). However, a majority of existing approaches are predicated on static, embedding-based scoring, exhibiting inherent limitations in capturing contextual dependencies and relational dynamics. Addressing this gap, we propose the Flow-Modulated Scoring (FMS) framework. FMS comprises two principal components: (1) a semantic context learning module that encodes context-sensitive entity representations, and (2) a conditional flow-matching module designed to learn the dynamic transformation from a head to a tail embedding, governed by the aforementioned context. The resultant predictive vector field, representing the context-informed relational path, serves to dynamically refine the initial static score of an entity pair. Through this synergy of context-aware static representations and conditioned dynamic information, FMS facilitates a more profound modeling of relational semantics. Comprehensive evaluations on several standard benchmarks demonstrate that our proposed method surpasses prior state-of-the-art results.

Problem

• Enhancing Knowledge Graph Completion with dynamic relational modeling
• Addressing limitations of static embedding-based scoring methods
• Improving contextual dependency capture in relation semantics

Innovation

• Semantic context learning for entity representations
• Conditional flow-matching for dynamic transformations
• Dynamic refinement of static scores via vector fields