档案文本事实粒子解构：概念、模型、方法与实证

摘要

传统档案管理与服务将文献作为基本信息单元，在奠定我国档案事业根基的同时存在流程断链、需求错位、支持缺位、性能瓶颈等不足，难以有效满足人工智能时代的档案精准化利用需求。本文致力于应对档案文献管理面临的现实挑战，基于档案事实语义颗粒化数据表征的视角，提出“档案文本事实粒子解构”的学术概念，构造事实逻辑模型，探索相关技术方法，并以实例验证其有效性。档案文本事实粒子解构是档案管理机构在档案预处理文本的基础上，参照事实逻辑 模型，通过对相关实体、属性和关系的识别和RDF表征，将档案文献的事实信息转换为颗粒化的准等义记录因子数据集合的技术过程，其应用有望驱动我国档案事业的智能化转型，并从整体上重塑传统档案文献服务的信息生态。

关键词：档案事实检索文本解构语义网记录因子

Abstract

Traditional archival management and services take documents as basic information unit. While laying the foundation for China's archival endeavors, this approach suffers from fragmented workflows, misaligned demands, insufficient support, and performance bottlenecks, making it difficult to meet the need for precise archival utilization in the AI era effectively. This paper aims to address the above practical challenges that archival document management faces. From the perspective of granular semantic data representation of archival facts, it proposes the academic concept of archival text fact particle deconstruction, constructs a fact-logic model, explores relevant technical methods, and validates their effectiveness through case studies. Archival text fact particle deconstruction is a technical process carried out by archival institutions, based on preprocessed texts and guided by the fact-logic model, identify relevant entities, attributes, and relationships, and represent them by RDF (Resource Description Framework) format, then transforms factual semantic information of archival document into granular, quasi-isomorphic set of record factors data. Its application is expected to drive the intelligent transformation of China's archival sector and holistically reshape the information ecology of traditional archival document services.

Key words: archive fact retrieval; text deconstruction; semantic web; record factors

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