Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion
Siyuan Li, Ruitong Liu, Yan Wen, Te Sun, Andi Zhang, Yanbiao Ma, Xiaoshuai Hao

TL;DR
The paper introduces Flow-Modulated Scoring (FMS), a novel dynamic framework that enhances knowledge graph completion by integrating static semantic contexts with a learned flow to better model relations.
Contribution
FMS unifies static semantic embeddings with a dynamic flow mechanism, offering a new paradigm for more accurate and parameter-efficient knowledge graph completion.
Findings
Achieves near-perfect MRR of 99.8% on FB15k-237
Outperforms baselines in inductive entity prediction
Uses only 0.35M parameters for high performance
Abstract
Knowledge graph completion demands effective modeling of multifaceted semantic relationships between entities. Yet, prevailing methods, which rely on static scoring functions over learned embeddings, struggling to simultaneously capture rich semantic context and the dynamic nature of relations. To overcome this limitation, we propose the Flow-Modulated Scoring (FMS) framework, conceptualizing a relation as a dynamic evolutionary process governed by its static semantic environment. FMS operates in two stages: it first learns context-aware entity embeddings via a Semantic Context Learning module, and then models a dynamic flow between them using a Conditional Flow-Matching module. This learned flow dynamically modulates a base static score for the entity pair. By unifying context-rich static representations with a conditioned dynamic flow, FMS achieves a more comprehensive understanding…
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