基于文本和多视角局部结构特征的知识图谱推理

doi:10.19678/j.issn.1000-3428.0069741

计算机工程 ›› 2025, Vol. 51 ›› Issue (11): 80-89. doi: 10.19678/j.issn.1000-3428.0069741

基于文本和多视角局部结构特征的知识图谱推理

刘海¹^,², 石佛波¹, 张昭理¹^,*(), 何嘉文¹, 李家豪¹

1. 华中师范大学人工智能教育学部, 湖北武汉 430000
2. 华中师范大学深圳研究院, 广东深圳 518000

收稿日期:2024-04-15 修回日期:2024-06-25 出版日期:2025-11-15 发布日期:2025-11-26
通讯作者: 张昭理
基金资助:
科技部2021年度"社会治理与智慧社会科技支撑"重点专项(2021YFC3340802); 国家自然科学基金(62577020); 国家自然科学基金(62477024); 国家自然科学基金(62377037); 国家自然科学基金(62277041); 国家自然科学基金(62173286); 国家自然科学基金(62177019); 国家自然科学基金(62177018); 中国高等教育学会高等教育研究专项(23XXK0403); 湖北省教育厅哲学社会科学研究项目(23G130); 湖北省教育厅科学技术研究计划指导性项目(B2023316); 湖北省自然科学基金(2025AFD621); 湖北省自然科学基金(2022CFB971); 湖北省自然科学基金(2022CFB529); 湖北省教育厅哲学社会科学研究项目(23G130); 江西省自然科学基金(20252BAC220007); 江西省自然科学基金(20252BAC240201); 江西省自然科学基金(20242BAB2S107); 江西省自然科学基金(20232BAB212026); 江西省高等学校教育教学改革研究课题(JXJG-23-27-6); 深圳市自然科学基金面上项目(JCYJ20230807152900001); 广东省基础与应用基础研究基金(2025A1515010266); 华中师范大学中央高校基本科研业务费专项资金(CCNU25ai012)

Knowledge Graph Reasoning Based on Textual and Multi-perspective Local Structural Features

LIU Hai¹^,², SHI Fobo¹, ZHANG Zhaoli¹^,*(), HE Jiawen¹, LI Jiahao¹

1. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430000, Hubei, China
2. Shenzhen Research Institute, Central China Normal University, Shenzhen 518000, Guangdong, China

Received:2024-04-15 Revised:2024-06-25 Online:2025-11-15 Published:2025-11-26
Contact: ZHANG Zhaoli

摘要/Abstract

摘要：

近年来, 知识图谱已经逐渐成为问答系统、信息检索和推荐系统等下游任务的基石。知识图谱推理作为知识图谱技术中的一项关键研究, 其推理结果的准确性决定了知识图谱的质量和服务效果。当前知识图谱推理研究主要集中于以知识嵌入作为知识载体的方式, 通过强大的神经网络模型来学习可表示事实知识隐含语义的实体和关系嵌入。面对当前海量异质知识涌现且持续增长的现状, 知识图谱中出现了知识结构缺失、知识分布长尾效应显著和推理过程可解释性弱等挑战。为此, 本研究提出一种基于文本和多视角局部结构特征的知识图谱推理模型TSNet, 通过有效融合知识图谱中实体-关系文本特征和多视角局部结构特征, 缓解了知识图谱中的结构缺失和数据长尾分布问题。实验结果表明, TSNet模型在4个常用公开数据集FB15k、WN18、FB15k-237和WN18RR上均获得了有竞争力的结果。

关键词: 知识图谱推理, 预训练模型, 特征融合, 结构特征

Abstract:

In recent years, knowledge graphs have gradually become the cornerstone of downstream tasks such as question answering, information retrieval, and recommendation systems. Knowledge graph reasoning is a key research topic in knowledge graph technology, and the accuracy of its reasoning results determines the quality of the knowledge graph and the effectiveness of its services. Recent research on knowledge graph reasoning has mainly focused on using knowledge embeddings as carriers of knowledge, as well as learning entity and relation embeddings that can represent the implicit semantics of factual knowledge through powerful neural network models. The emergence of massive heterogeneous knowledge and its continuous growth have brought about challenges such as missing knowledge structures, a long-tail distribution of knowledge (with significant skewness), and weak interpretability in knowledge graph reasoning. This study proposes TSNet, a novel knowledge graph reasoning model based on textual and multi-perspective local structural features. Effectively fusing entity and relation text features and multi-perspective local structure features in the knowledge graph helps mitigate the problem of missing structures and the long-tail distribution of data. Experimental results demonstrate that TSNet achieves competitive results on four common knowledge graph reasoning datasets: FB15k, WN18, FB15k-237, and WN18RR.

Key words: knowledge graph reasoning, pre-trained model, feature fusion, structural feature

刘海, 石佛波, 张昭理, 何嘉文, 李家豪. 基于文本和多视角局部结构特征的知识图谱推理[J]. 计算机工程, 2025, 51(11): 80-89.

LIU Hai, SHI Fobo, ZHANG Zhaoli, HE Jiawen, LI Jiahao. Knowledge Graph Reasoning Based on Textual and Multi-perspective Local Structural Features[J]. Computer Engineering, 2025, 51(11): 80-89.

https://www.ecice06.com/CN/Y2025/V51/I11/80

图/表 10

图1 TSNet模型整体架构

Fig.1 Overall architecture of the TSNet model

图2 批量大小参数实验结果

Fig.2 Experimental results of batch size parameters

图3 嵌入维度与卷积核数量分析

Fig.3 Analysis of embedding dimensions and convolutional kernel numbers

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