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Economies of Space: Opening up Historical Finding Aids

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Economies of Space: Opening up Historical Finding Aids

Poster Session
Authors
Affiliations

Lucas Burkart

University of Basel

Tobias Hodel

University of Bern

Benjamin Hitz

University of Basel

Aline Vonwiller

University of Basel

Ismail Prada Ziegler

University of Bern

Jonas Aeby

University of Basel

Katrin Fuchs

University of Basel

Published

September 12, 2024

Doi

10.5281/zenodo.13908083

In the realm of historical data processing, machine learning has emerged as a game-changer, enabling the analysis of vast archives and complex finding aids on an unprecedented scale. One intriguing case study exemplifying the potential of these techniques is the digitization of the Historical Land Registry of the City of Basel (=Historisches Grundbuch Basel, HGB). The HGB, compiled around the turn of the 20th century, contains a wealth of historical data meticulously collected on index cards. Each card represents a transaction or entry from source documents, and the structured data reflects the conventions and interests of its creators. This inherent complexity has set the stage for a multifaceted exploration, encompassing text recognition, specifically for handwritten materials, and information extraction, particularly event extraction.

One of the key accomplishments of this endeavor is the successful application of machine learning algorithms to decipher handwritten content, resulting in a remarkably low character error rate of just 4%. This breakthrough paves the way for extracting valuable information, such as named entities (persons, places, organizations), their relationships, and mentioned events, through specialized language models.

When combined with property information, the extracted data offers a unique opportunity to visualize historical events and transactions on Geographical Information Systems. This process allows for analyzing normative and semantic shifts in the real estate market over time, shedding light on historical changes in language and practice.

Ultimately, this project signifies a milestone in historical data analysis. Machine learning techniques have matured so that even extensive datasets and intricate finding aids can be effectively processed. As a result, innovative approaches to large-scale historical data analysis are now within reach, offering new perspectives on dynamic urban economies during pre-modern times. This venture showcases how technological approaches and humanities deliberations go hand in hand to understand complex patterns in economic history.

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Citation

BibTeX citation:
@misc{burkart2024,
  author = {Burkart, Lucas and Hodel, Tobias and Hitz, Benjamin and
    Vonwiller, Aline and Prada Ziegler, Ismail and Aeby, Jonas and
    Fuchs, Katrin},
  editor = {Baudry, Jérôme and Burkart, Lucas and Joyeux-Prunel,
    Béatrice and Kurmann, Eliane and Mähr, Moritz and Natale, Enrico and
    Sibille, Christiane and Twente, Moritz},
  title = {Economies of {Space:} {Opening} up {Historical} {Finding}
    {Aids}},
  date = {2024-09-12},
  url = {https://digihistch24.github.io/submissions/poster/466/},
  doi = {10.5281/zenodo.13908083},
  langid = {en}
}
For attribution, please cite this work as:
Burkart, Lucas, Tobias Hodel, Benjamin Hitz, Aline Vonwiller, Ismail Prada Ziegler, Jonas Aeby, and Katrin Fuchs. 2024. “Economies of Space: Opening up Historical Finding Aids.” Edited by Jérôme Baudry, Lucas Burkart, Béatrice Joyeux-Prunel, Eliane Kurmann, Moritz Mähr, Enrico Natale, Christiane Sibille, and Moritz Twente. Digital History Switzerland 2024: Book of Abstracts. https://doi.org/10.5281/zenodo.13908083.
Source Code
---
submission_id: 466
categories: 'Poster Session'
title: 'Economies of Space: Opening up Historical Finding Aids'
author:
  - name: Lucas Burkart
    orcid: 0000-0002-9011-5113
    email: lucas.burkart@unibas.ch
    affiliations:
      - University of Basel
  - name: Tobias Hodel
    orcid: 0000-0002-2071-6407
    email: tobias.hodel@unibe.ch
    affiliations:
      - University of Bern
  - name: Benjamin Hitz
    orcid: 0000-0002-3208-4881
    email: benjamin.hitz@unibas.ch
    affiliations:
      - University of Basel
  - name: Aline Vonwiller
    orcid: 0009-0001-2098-9237
    email: a.vonwiller@unibas.ch
    affiliations:
      - University of Basel
  - name: Ismail Prada Ziegler
    orcid: 0000-0003-4229-8688
    email: ismail.prada@unibe.ch
    affiliations:
      - University of Bern
  - name: Jonas Aeby
    email: jonas.aeby@unibas.ch
    affiliations:
      - University of Basel
  - name: Katrin Fuchs
    email: katrin.fuchs@unibas.ch
    affiliations:
      - University of Basel
date: 09-12-2024
doi: 10.5281/zenodo.13908083
---

In the realm of historical data processing, machine learning has emerged as a game-changer, enabling the analysis of vast archives and complex finding aids on an unprecedented scale. One intriguing case study exemplifying the potential of these techniques is the digitization of the Historical Land Registry of the City of Basel (=Historisches Grundbuch Basel, HGB).
The HGB, compiled around the turn of the 20th century, contains a wealth of historical data meticulously collected on index cards. Each card represents a transaction or entry from source documents, and the structured data reflects the conventions and interests of its creators. This inherent complexity has set the stage for a multifaceted exploration, encompassing text recognition, specifically for handwritten materials, and information extraction, particularly event extraction.

One of the key accomplishments of this endeavor is the successful application of machine learning algorithms to decipher handwritten content, resulting in a remarkably low character error rate of just 4%. This breakthrough paves the way for extracting valuable information, such as named entities (persons, places, organizations), their relationships, and mentioned events, through specialized language models.

When combined with property information, the extracted data offers a unique opportunity to visualize historical events and transactions on Geographical Information Systems. This process allows for analyzing normative and semantic shifts in the real estate market over time, shedding light on historical changes in language and practice.

Ultimately, this project signifies a milestone in historical data analysis. Machine learning techniques have matured so that even extensive datasets and intricate finding aids can be effectively processed. As a result, innovative approaches to large-scale historical data analysis are now within reach, offering new perspectives on dynamic urban economies during pre-modern times. This venture showcases how technological approaches and humanities deliberations go hand in hand to understand complex patterns in economic history.
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