ATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopedias

• URL: http://arxiv.org/abs/2605.02466v1
• Date: Mon, 04 May 2026 11:08:59 GMT
• Title: ATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopedias
• Abstract summary: We build a pipeline to restore the text structure of Swedish encyclopedia.<n>We extract the headwords and identify entries; categorize the entities; match entries across editions; and link entries to a Wikidata item.<n>This shows that an automated approach to digitized historical knowledge is possible.
• Score: 0.9332987715848714
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Diagnosing and Mitigating Semantic Inconsistencies in Wikidata's Classification Hierarchy [1.4705700441788643]
  Wikidata is the largest open knowledge graph on the web, encompassing over 120 million entities.<n>This study proposes and applies a novel validation method to confirm the presence of classification errors and over-generalized subclass links.<n>We develop a system that allows users to inspect the taxonomic relationships of arbitrary Wikidata entities.
  arXiv  Detail & Related papers  (2025-11-07T02:09:00Z)
• Towards a Brazilian History Knowledge Graph [50.26735825937335]
  We construct a knowledge graph for Brazilian history based on the Brazilian Dictionary of Historical Biographies (DHBB) and Wikipedia/Wikidata. We show that many terms/entities described in the DHBB do not have corresponding concepts (or Q items) in Wikidata.
  arXiv  Detail & Related papers  (2024-03-28T22:05:32Z)
• Wikidata as a seed for Web Extraction [4.273966905160028]
  We present a framework that is able to identify and extract new facts that are published under multiple Web domains. We take inspiration from ideas that are used to extract facts from textual collections and adapt them to extract facts from Web pages. Our experiments show that we can achieve a mean performance of 84.07 at F1-score.
  arXiv  Detail & Related papers  (2024-01-15T16:35:52Z)
• WikiIns: A High-Quality Dataset for Controlled Text Editing by Natural Language Instruction [56.196512595940334]
  We build and release WikiIns, a high-quality controlled text editing dataset with improved informativeness. With the high-quality annotated dataset, we propose automatic approaches to generate a large-scale silver'' training set.
  arXiv  Detail & Related papers  (2023-10-08T04:46:39Z)
• Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences [68.8204255655161]
  We propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level. The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia. We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models.
  arXiv  Detail & Related papers  (2022-10-23T08:34:33Z)
• WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs [66.88232442007062]
  We introduce WikiDes, a dataset to generate short descriptions of Wikipedia articles. The dataset consists of over 80k English samples on 6987 topics. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions.
  arXiv  Detail & Related papers  (2022-09-27T01:28:02Z)
• Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking [76.00737707718795]
  We propose a novel candidate retrieval paradigm based on entity profiling. We use the profile to query the indexed search engine to retrieve candidate entities. Our approach complements the traditional approach of using a Wikipedia anchor-text dictionary.
  arXiv  Detail & Related papers  (2022-02-27T17:38:53Z)
• Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History [5.727994421498849]
  We present Wikidated 1.0, a dataset of Wikidata's full revision history. To the best of our knowledge, it constitutes the first large dataset of an evolving knowledge graph.
  arXiv  Detail & Related papers  (2021-12-09T15:54:03Z)
• Assessing the quality of sources in Wikidata across languages: a hybrid approach [64.05097584373979]
  We run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata, and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web.
  arXiv  Detail & Related papers  (2021-09-20T10:06:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.