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SFPL: Sensitivity feature perception learning based on stochastic differential equations for temporal knowledge graph completion
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AI
DOI：10.1016/j.knosys.2025.115188.png)
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Abstract
En 中文
Temporal Knowledge Graph Completion (TKGC) faces significant challenges due to the facts in Temporal Knowledge Graphs (TKGs) undergoing complex changes over time. Existing TKGC studies struggle to adequately model these dynamic temporal changes. They also overlook the varying frequencies at which entities and relations change over time, leading to suboptimal performance. We propose Sensitivity Feature Perception Learning (SFPL). Leveraging the ability of Stochastic Differential Equations (SDE) to simulate continuous temporal changes, SFPL explores the unique characteristics exhibited by entities and relations over time. It further enhances the learning of their characteristic attributes through a spatiotemporal feature interaction approach. Specifically, SFPL represents the temporal change frequencies of entities and relations as sensitivity features, which are modeled separately using SDE. To better represent the different influencing perspectives of time on entities and relations, SFPL utilizes Ito's lemma and the Fokker-Planck equation. They allow SFPL to better reflect the states of entities and the strengths of relations. Additionally, SFPL facilitates interaction between the sensitivity features of facts and their intrinsic attributes. Thereby, it delves deeper into learning complex representations of characteristic attributes. Experiments show that SFPL demonstrates remarkable performance on six datasets. Compared to baseline models, it improves Mean Reciprocal Rank (MRR) by 25.1%.
Keywords:
Temporal knowledge graph
Sensitivity feature
Spatiotemporal feature interaction