基于四元数表示与显式历史增强的时序知识图谱补全方法

doi:10.16451/j.cnki.issn1003-6059.202607005

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Abstract Existing temporal knowledge graph completion models struggle to effectively model the deep interactions between semantic information and multi-granularity temporal information and lack explicit the modeling of temporal sensitivity of historical information. To solve these problems, an approach for quaternion-based representation and explicit historical enhancement for temporal knowledge graph completion(QR-EH) is proposed. First, the semantic information of entities and relations, and the multi-granularity temporal information are mapped into the quaternion space. Deep interactions between semantics and time are achieved through Hamiltonian products. Then, an explicit historical retrieval module is designed. A temporal modulation mechanism is defined by this module. Time-sensitive historical patterns are accurately captured by weighting historical recurring events. Finally, an adaptive score fusion module is constructed. Global spatio-temporal information and historical recurring information are weighted and fused by this module. The final prediction results are generated. Experiments on three publicly available datasets demonstrate that QR-EH deeply mines temporal evolution and historical recurrence patterns and exhibits good generalization and interpretability.

Key words： Temporal Knowledge Graph Completion Multi-granularity Time Quaternion Temporal Modulation Mechanism History-Enhanced Score Coefficient

Received: 13 March 2026

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.62201377), Shanxi Scholarship Council of China(No.2024-61)

Corresponding Authors: XIE Jun, Ph.D., associate professor. Her research interests include natural language processing, granular computing, rough sets, data mining and machine learning.

About author:: LÜ Jiaqi, Master student. Her research interests include knowledge graphs and artificial intelligence.
WANG Li, Ph.D., professor. Her research interests include social computing and data mining.
WANG Dantong, Master student. His research interests include knowledge graphs and recommender systems.
LUO Xiongyan, Master student. Her research interests include knowledge graphs and recommender systems.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202607005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I7/636

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