一个适应性语义检索框架用于数字图书馆,集成图形神经网络,本体学和用户行为
1Southwest University Library, Southwest University, Beibei, Chongqing, 400715, China. 17764889362@163.com.
Scientific reports
|November 18, 2025
概括
本研究介绍了使用图形神经网络 (GNN) 和数字图书馆的用户行为进行适应性语义检索框架. 它通过平衡领域知识与用户相关性来显著改善信息发现.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 人工智能的人工智能
背景情况:
- 传统的知识组织在数字图书馆中使用静态方法,无法捕捉复杂的关系或适应用户需求.
- 现有的系统在与多维关系和不断变化的用户信息搜索行为作斗争.
- 在将正式知识结构与动态用户模式集成为适应性检索方面存在差距.
研究的目的:
- 为数字图书馆开发一种新的自适应语义检索框架.
- 集成图形神经网络 (GNN),本体知识和用户行为分析.
- 通过动态平衡域名语义和个性化的相关性来增强信息发现.
主要方法:
- 构建一个本体学驱动的知识图.
- 应用多关系GNN用于表示学习.
- 开发一个用户行为模型和一个适应性检索机制.
- 整合正式语义与实证的使用模式.
主要成果:
- 适应框架在实验中实现了81%的精度和85%的回忆.
- 与传统的检索模型相比,显著提高了性能.
- 该系统成功地平衡了域名语义与个性化的相关性信号.
结论:
- 拟议的框架为智能信息发现提供了一种统一的计算方法.
- 它将传统分类与以用户为中心的设计原则相结合,用于适应性知识组织.
- 这种方法提高了数字图书馆检索中的语义连贯性和响应性.
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