基于上下文语义与全局信息的局部引文推荐研究

doi:10.3772/j.issn.1000-0135.2025.10.005

情报学报

2025, Vol. 44

Issue (10): 1272-1286 DOI: 10.3772/j.issn.1000-0135.2025.10.005

情报技术与应用

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基于上下文语义与全局信息的局部引文推荐研究

张晓娟, 马乐

四川大学公共管理学院，成都 610065

Local Citation Recommendation Based on Contextual Semantics and Global Information

Zhang Xiaojuan, Ma Le

School of Public Administration, Sichuan University, Chengdu 610065

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摘要局部引文推荐有助于研究人员更快速有效地获得参考文献。已有的局部引文推荐方法主要聚焦于如何根据有限的上下文来获得与之相关的候选引文，限制了模型推荐结果的准确度提升。本文尝试在融合上下文语义信息的基础上，进一步融合全局信息（如全局语义和全局关系信息）来提升局部引文推荐的准确度。首先，在经过预训练的SciBERT（scientific bidirectional encoder representations from transformers）模型基础上，通过自定义任务对其进行模型微调后，提取引文上下文语义信息。为了最大限度地利用论文标题和摘要所包含的高度概括的全局语义信息，分别利用Sentence-BERT模型和微调后的SciBERT模型对论文标题向量和论文摘要向量进行提取。其次，构建包括作者、论文和论文发表地（会议或期刊）三类节点的学术文献异构关系图，并通过关系图卷积网络（relational graph convolutional network，R-GCN）对三种不同关系类型（引用关系、写作关系以及发表关系）进行聚合来获得论文以及作者向量的嵌入表达。最后，将引文推荐转化为多分类问题，即将上下文语义信息、目标论文全局标题信息、目标论文全局摘要信息与嵌入了全局关系的目标论文向量和目标论文作者向量进行拼接作为推荐模型的输入，通过前馈神经网络（feedforward neural network，FFNN）+softmax进行训练，为给定上下文生成相应的候选引文列表。为进一步提高运算效率，本文尝试通过多设备分布式数据并行以及单设备模型压缩两种优化策略来降低模型运行的时间开销。实验结果表明，本文方法能有效提升局部引文推荐的准确度；在考虑引文上下文语义的基础上，在所有表征全局信息的向量（标题语义向量、摘要向量、作者向量和论文向量）中，论文向量对模型推荐性能的贡献最大；全局关系信息对模型推荐性能的贡献大于全局语义信息；在全局关系信息中，历史引用信息对局部引文推荐整体性能提升的贡献最大；两种模型优化方法均能提高模型的运行效率。

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关键词 ：上下文语义, 全局信息, 局部引文推荐, R-GCN模型

收稿日期: 2024-11-27

基金资助:国家社会科学基金一般项目“时间感知的个性化学术文献引文推荐研究”（21BTQ072）。

作者简介: 张晓娟，1985年生，博士，副教授，硕士生导师，主要研究方向为信息检索，E-mail：zxj0614@scu.edu.cn；马乐，2001年生，硕士研究生，主要研究领域为引文推荐；

引用本文:

张晓娟, 马乐. 基于上下文语义与全局信息的局部引文推荐研究[J]. 情报学报, 2025, 44(10): 1272-1286.
Zhang Xiaojuan, Ma Le. Local Citation Recommendation Based on Contextual Semantics and Global Information. 情报学报, 2025, 44(10): 1272-1286.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2025.10.005 或 https://qbxb.istic.ac.cn/CN/Y2025/V44/I10/1272

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