A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning
Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is...
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2595.pdf |
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| author | Weitong Liu Khairunnisa Hasikin Anis Salwa Mohd Khairuddin Meizhen Liu Xuechen Zhao |
| author_facet | Weitong Liu Khairunnisa Hasikin Anis Salwa Mohd Khairuddin Meizhen Liu Xuechen Zhao |
| author_sort | Weitong Liu |
| collection | DOAJ |
| description | Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is of great significance and presents considerable obstacles. While current models consider the fact changes over time and recognize that historical facts may recur, they often overlook the influence of past events on future predictions. Motivated by these considerations, this work introduces a novel temporal knowledge graph reasoning model, named Temporal Reasoning with Recurrent Encoding and Contrastive Learning (TRCL), which integrates recurrent encoding and contrastive learning techniques. The proposed model has the ability to capture the evolution of historical facts, generating representations of entities and relationships through recurrent encoding. Additionally, TRCL incorporates a global historical matrix to account for repeated historical occurrences and employs contrastive learning to alleviate the interference of historical facts in predicting future events. The TKG reasoning outcomes are subsequently derived through a time decoder. A quantity of experiments conducted on four benchmark datasets demonstrate the exceptional performance of the proposed TRCL model across a range of metrics, surpassing state-of-the-art TKG reasoning models. When compared to the strong baseline Time-Guided Recurrent Graph Network (TiRGN) model, the proposed TRCL achieves 1.03% improvements on ICEWS14 using mean reciprocal rank (MRR) evaluation metric. This innovative proposed method not only enhances the accuracy of TKG extrapolation, but also sets a new standard for robustness in dynamic knowledge graph applications, paving the way for future research and practical applications in predictive intelligence systems. |
| format | Article |
| id | doaj-art-286cbadb7d6745b4b9bfcedd1d9d2525 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-286cbadb7d6745b4b9bfcedd1d9d25252025-01-25T15:05:13ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e259510.7717/peerj-cs.2595A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learningWeitong Liu0Khairunnisa Hasikin1Anis Salwa Mohd Khairuddin2Meizhen Liu3Xuechen Zhao4School of Data and Computer Science, Shandong Women’s University, Shandong, ChinaDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaSchool of Data and Computer Science, Shandong Women’s University, Shandong, ChinaSchool of Data and Computer Science, Shandong Women’s University, Shandong, ChinaTemporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is of great significance and presents considerable obstacles. While current models consider the fact changes over time and recognize that historical facts may recur, they often overlook the influence of past events on future predictions. Motivated by these considerations, this work introduces a novel temporal knowledge graph reasoning model, named Temporal Reasoning with Recurrent Encoding and Contrastive Learning (TRCL), which integrates recurrent encoding and contrastive learning techniques. The proposed model has the ability to capture the evolution of historical facts, generating representations of entities and relationships through recurrent encoding. Additionally, TRCL incorporates a global historical matrix to account for repeated historical occurrences and employs contrastive learning to alleviate the interference of historical facts in predicting future events. The TKG reasoning outcomes are subsequently derived through a time decoder. A quantity of experiments conducted on four benchmark datasets demonstrate the exceptional performance of the proposed TRCL model across a range of metrics, surpassing state-of-the-art TKG reasoning models. When compared to the strong baseline Time-Guided Recurrent Graph Network (TiRGN) model, the proposed TRCL achieves 1.03% improvements on ICEWS14 using mean reciprocal rank (MRR) evaluation metric. This innovative proposed method not only enhances the accuracy of TKG extrapolation, but also sets a new standard for robustness in dynamic knowledge graph applications, paving the way for future research and practical applications in predictive intelligence systems.https://peerj.com/articles/cs-2595.pdfTemporal knowledge graph reasoningRecurrent encodingContrastive learning |
| spellingShingle | Weitong Liu Khairunnisa Hasikin Anis Salwa Mohd Khairuddin Meizhen Liu Xuechen Zhao A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning PeerJ Computer Science Temporal knowledge graph reasoning Recurrent encoding Contrastive learning |
| title | A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning |
| title_full | A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning |
| title_fullStr | A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning |
| title_full_unstemmed | A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning |
| title_short | A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning |
| title_sort | temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning |
| topic | Temporal knowledge graph reasoning Recurrent encoding Contrastive learning |
| url | https://peerj.com/articles/cs-2595.pdf |
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