ATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopedias
For digital humanities researchers, this work provides an automated method to unlock structured knowledge from historical encyclopedias, but the results are incremental and domain-specific.
The authors built a pipeline to restore structure in digitized encyclopedias, extracting headwords, classifying entries, matching across editions, and linking to Wikidata. Applied to four editions of Nordisk familjebok, they achieved 97.8% F1 for headword extraction and 93.4% for classification, with 93% precision on cross-edition matching and 85% precision on Wikidata linking.
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.