一个基于循环编码和对比学习的时间知识图推理模型
Weitong Liu1,2, Khairunnisa Hasikin3, Anis Salwa Mohd Khairuddin2
1School of Data and Computer Science, Shandong Women's University, Shandong, China.
PeerJ. Computer science
|February 3, 2025
概括
本研究介绍了以重复编码和对比学习 (TRCL) 的时间推理,这是时间知识图推理的新模型. 通过有效地从历史数据中学习和减少过去事实干扰,TRCL改善了未来事件预测.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 时间知识图 (TKG) 对于智能系统中模拟时间演变的数据至关重要.
- 在TKG中预测未来事件 (外推) 是一个挑战,因为现有的模型往往忽略了过去事件的影响.
研究的目的:
- 开发一种新的时间知识图推理模型,TRCL,以提高推断准确度.
- 解决当前模型在捕捉历史影响对未来预测的局限性.
主要方法:
- TRCL集成了循环编码,以捕捉时间动态和实体/关系演变.
- 全球历史矩阵为重复的过去事件负责.
- 对比式学习在未来事件预测过程中最大限度地减少了历史数据的干扰.
主要成果:
- 在四个基准数据集上,TRCL表现出卓越的表现,超过了最先进的模型.
- 与TiRGN基线相比,该模型在ICEWS14数据集上的平均互惠排名 (MRR) 得到了1.03%的改善.
结论:
- TRCL显著提高了时间知识图谱推断的准确性和稳定性.
- 该模型为动态知识图应用程序和预测智能系统建立了新的基准.
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