Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion

• URL: http://arxiv.org/abs/2506.23137v2
• Date: Tue, 01 Jul 2025 17:51:20 GMT
• Title: Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion
• Authors: Siyuan Li, Ruitong Liu, Yan Wen, Te Sun,
• Abstract summary: Flow-Modulated Scoring (FMS) is a framework for capturing contextual dependencies and relational dynamics.<n>FMS comprises two principal components: (1) a semantic context learning module that encodes context-sensitive entity representations, and (2) a conditional flow-matching module.<n>Our proposed method surpasses prior state-of-the-art results.
• Score: 18.480268023065747
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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