基于邻域粒度与三支决策的知识表示学习方法

doi:10.6040/j.issn.1671-9352.4.2024.534

摘要/Abstract

摘要： 提出了一种基于邻域粒度与三支决策理论的知识表示学习方法,该方法采用2阶段的框架式增强算法,第1阶段通过知识表示学习方法拟合知识图谱中的节点与关系,映射其中蕴含的语义信息进入低维向量空间;第2阶段,通过划分低维向量表示的邻域粒度,捕捉和利用语义信息中的潜藏相似关系,并辅以三支决策对邻域粒度所挖掘的相似语义信息进行精准的划分,再将所挖掘出的潜藏信息对模型进行重训练,提升知识表示学习方法的准确性与鲁棒性。本文选定5种经典的知识表示学习模型,并在4个公开的大型知识图谱数据集上进行实验,通过实验结果验证了本方法的有效性。

关键词: 知识图谱, 知识表示学习, 邻域粒度, 三支决策

Abstract: A knowledge representation learning method based on neighborhood granularity and three-way decision theory(NGTwD)is proposed. The method is implemented using a two-stage enhancement algorithm framework. In the first stage, knowledge representation learning is utilized to fit the nodes and relations in the knowledge graph, and map the embedded semantic information into a low-dimensional vector space. To better capture and exploit the latent similarities in the semantic information, the neighborhood granularity of the low-dimensional vector representations is divided in the second stage. This process is further complemented by applying three-way decision theory to precisely segment the similar semantic information. The extracted latent information is then used to retrain the model, thereby improving the accuracy and robustness of the knowledge representation learning method. Five classic knowledge representation learning models are selected, and experiments are conducted on four large publicly available knowledge graph datasets. The effectiveness of the proposed method is validated through the experimental results.

Key words: knowledge graph, knowledge graph representation learning, neighborhood granularity, three-way decision

中图分类号:

TP391

钱文彬,彭嘉豪,蔡星星. 基于邻域粒度与三支决策的知识表示学习方法[J]. 《山东大学学报(理学版)》, 2025, 60(7): 94-103.

QIAN Wenbin, PENG Jiahao, CAI Xingxing. Knowledge graph representation learning based on neighborhood granularity and three-way decision[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2025, 60(7): 94-103.

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