自动化还原瑞典百科全书结构，实现词条跨版本追踪与知识图谱链接。
ATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopedias
- 构建流水线还原古籍文本结构，提取词条并分类。
- 词条提取F1达97.8%，跨版匹配准确率93%。
- 适合历史知识研究者与数字人文项目使用。
古籍百科的数字化是提升历史知识可及性的关键步骤，但多数仅依赖光学字符识别，未能利用其内在结构。许多百科存在多个版本，反映知识演变过程，而原始文本缺乏结构，难以追踪版本间变化。本文构建了一套流水线，用于恢复文本结构：提取词条、识别条目、实体分类、跨版本匹配以及链接至Wikidata。以1876至1951年出版的权威瑞典百科《Nordisk familjebok》四版为对象，实现了97.8%的词条提取F1值，93.4%的词条分类F1值；小规模评估中，跨版本匹配精度达93%，Wikidata链接精度85%、召回率16.5%。结果表明，自动化处理数字化历史知识可行，有助于通用知识保存与知识传播理解。数据与代码已公开。
原文摘要 · Abstract (English)
The digitization of old encyclopedias represents an important step to improve access to historically structured knowledge. Often, however, this process does not go beyond an optical character recognition, leaving all the underlying structure unexploited. In addition, many encyclopedias had multiple editions reflecting the evolution of knowledge. The lack of structure in the raw text makes it difficult to track changes across these editions. In this work, we built a pipeline to restore the text structure, where we extract the headwords and identify entries; categorize the entities; match entries across editions; and link entries to a Wikidata item. We applied this pipeline to the four major editions of \textit{Nordisk familjebok}, an authoritative Swedish encyclopedia published between 1876 and 1951. We could extract the headwords with an F1 score of 97.8\% and we obtained an F1 score of 93.4\% on the headword classification. On a small-scale evaluation, we reached a 93\% precision on the cross-edition matching, 85\% precision and 16.5\% recall on the Wikidata linking. This shows that an automated approach to digitized historical knowledge is possible. This should facilitate the preservation of general knowledge and the understanding of knowledge transmission. The datasets and programs are available online.
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