将OPACs转变为智能发现系统：一种由AI驱动、基于知识图谱的智能OPAC用于数字图书馆
Transforming OPACs into Intelligent Discovery Systems: An AI-Powered, Knowledge Graph-Driven Smart OPAC for Digital Libraries
AI总结：
本文提出一种智能OPAC框架，利用人工智能和知识图谱技术将传统OPAC转变为智能发现系统，通过语义搜索、主题过滤和知识图谱可视化提升用户体验，整合开放学术数据源并应用语义嵌入以提高检索效率和相关性。
摘要：
传统在线公共访问目录（OPAC）由于学术文献的快速增长而变得效率低下。传统搜索方法如关键词索引和布尔查询往往无法支持高效的知识发现。本文提出了一种智能OPAC框架，利用人工智能和知识图谱技术将传统OPAC转变为智能发现系统。该框架实现了语义搜索、主题过滤和基于知识图谱的可视化，以增强用户交互和探索。它整合了多个开放的学术数据源，并应用语义嵌入以提高相关性和上下文理解。系统支持探索性搜索、语义导航和基于用户定义主题的精细结果过滤。定量评估显示在检索效率、相关性和信息过载减少方面有所提升。所提出的方法对现代数字图书馆服务现代化具有实际意义，并支持下一代研究流程。未来工作包括以用户为中心的评估、个性化和动态知识图谱更新。
英文摘要：
Traditional Online Public Access Catalogues (OPACs) are becoming less effective due to the rapid growth of scholarly literature. Conventional search methods, such as keyword indexing and Boolean queries, often fail to support efficient knowledge discovery. This paper proposes a Smart OPAC framework that transforms traditional OPACs into intelligent discovery systems using artificial intelligence and knowledge graph techniques. The framework enables semantic search, thematic filtering, and knowledge graph-based visualization to enhance user interaction and exploration. It integrates multiple open scholarly data sources and applies semantic embeddings to improve relevance and contextual understanding. The system supports exploratory search, semantic navigation, and refined result filtering based on user-defined themes. Quantitative evaluation demonstrates improvements in retrieval efficiency, relevance, and reduction of information overload. The proposed approach offers practical implications for modernizing digital library services and supports next-generation research workflows. Future work includes user-centric evaluation, personalization, and dynamic knowledge graph updates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
