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对时间知识图查询放松的实体替代策略

Luyi Bai¹, Jixuan Dong², Lin Zhu²

¹School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China; Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China.

Neural networks : the official journal of the International Neural Network Society

|May 10, 2025

概括

本研究介绍了实体替代时间知识图查询放松 (ER-TQR) 的新方法,该新方法可以提高时间知识图查询的灵活性. 通过替换实体,ER-TQR有效地处理带有空结果的查询,优于现有技术.

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
知识表示知识表示

背景情况:

时间知识图 (TKG) 查询对于获取时间有限的信息至关重要.
当前的TKG查询方法经常失败,查询没有结果,限制了它们的实际应用.
提高查询灵活性,特别是对于空结果场景,是一个开放的研究挑战.

研究的目的:

提出一种新的查询放松方法,即实体替代时间知识图查询放松 (ER-TQR),专门用于返回空结果的TKG查询.
提高时间知识图查询系统的灵活性和稳定性.
提高时间知识图查询结果的准确性和相关性.

主要方法:

开发了ER-TQR,这是一个采用实体替换策略的查询放松技术.
利用来自变压器的双向编码器表示 (BERT) 来进行增强的语义查询嵌入.
整合了一种双向门式反复单元 (Bi-GRU) 模型,以基于语义和时间对齐来识别和替换有问题的实体.

主要成果:

在处理空结果的时间知识图查询时,ER-TQR表现出卓越的性能.
该方法成功地用语义和时间相关的候选项替换了不兼容的实体,从而保留了查询意图.
实验结果显示,ER-TQR在不同数据集的5个评估指标中的4个中超过了现有的查询放松方法.

结论:

在时间知识图查询放松方面,ER-TQR提供了显著的进步,特别是对于空结果集.
拟议的实体替代策略与BERT和Bi-GRU嵌入相结合,有效地提高了查询的灵活性和准确性.
这项研究为更强大,更易于使用的时间知识图查询系统铺平了道路.

关键词:

贝尔特 (BERT) 公司实体更换实体的替代神经网络的神经网络的神经网络查询放松放松的要求时间知识图查询时间知识图查询

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