让历史监狱档案可对话查询并动态构建知识图谱
Conversational Retrieval and On-the-Fly Knowledge Modeling of Historical Penitentiary Repression Records
- 用图结构实时存储专家知识与检索结果
- 支持跨文档长依赖查询和关系发现
- 适合历史文献研究者与数字人文学者
近年来，数字图书馆越来越倾向于通过检索增强生成（RAG）实现自然语言对话式信息访问。尽管这类方法在基于单个记录的抽取任务中表现良好，但在整体解读文档集合和动态融入专家知识方面仍显不足。本文提出一个面向历史数字图书馆的文档分析系统，支持即时知识建模。系统通过图结构存储由专家档案员或文档检索过程生成的事实。在持续的专业交互中，语言模型不仅能从原始文档中检索信息，还能访问已建模的知识，图索引作为语言模型的内存。这使得系统能够处理涉及多文档长期依赖的复杂查询、发现关联关系，并整合原始资料中未明确记载的专家知识。最终，该方法显著提升了信息生成的丰富性与全面性。
原文摘要 · Abstract (English)
Recent developments in digital libraries increasingly favor conversational and natural language access to information through Retrieval-Augmented Generation (RAG). Although these approaches are effective for extractive tasks grounded in individual records, they remain limited in their ability to interpret document collections holistically and to incorporate expert knowledge dynamically. In this article, we present a document analysis system designed for the management of historical digital libraries that supports on-the-fly knowledge modeling. The system is equipped with the capability to store facts produced either by expert archivists or derived from document retrieval processes within a graph-based structure. Through continuous professional interaction, the system can retrieve information not only from primary sources such as documents, but also from previously modeled knowledge, with the graph-based index acting as a memory for the language model to access. This enables increasingly complex queries involving long-term dependencies across documents, link discovery, and the integration of expert knowledge that may not be explicitly present in the original sources. As a result, the proposed approach facilitates the generation of richer and more comprehensive information.
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