Historical Texts into Structured Knowledge Graphs : Introducing Computer-Assisted Semantic Text Modelling in InkVisitor

ZBÍRAL, David, Robert Laurence John SHAW, Tomáš HAMPEJS and Adam MERTEL. Historical Texts into Structured Knowledge Graphs : Introducing Computer-Assisted Semantic Text Modelling in InkVisitor. In Social Networks in Medieval and Renaissance Studies: Thirty Years after Robust Action, Vienna, 21-23 June 2023. 2023.

Basic information

Original name

Historical Texts into Structured Knowledge Graphs : Introducing Computer-Assisted Semantic Text Modelling in InkVisitor

Name (in English)

Historical Texts into Structured Knowledge Graphs : Introducing Computer-Assisted Semantic Text Modelling in InkVisitor

Authors

ZBÍRAL, David, Robert Laurence John SHAW, Tomáš HAMPEJS and Adam MERTEL

Edition

Social Networks in Medieval and Renaissance Studies: Thirty Years after Robust Action, Vienna, 21-23 June 2023, 2023

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Other information

Type of outcome

Prezentace na konferencích

Confidentiality degree

není předmětem státního či obchodního tajemství

Keywords (in Czech)

sémantické modelování textu; digital humanities; náboženský disent

Keywords in English

semantic text modelling; digital humanities; inquisition; religious dissidence

Tags

International impact, Reviewed

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Abstract

V originále

The paper outlines the methods employed in the DISSINET project for generating structured knowledge graphs from historical data, shedding light on the challenges faced by historians and researchers. The authors introduce Computer-Assisted Semantic Text Modelling (a human-controlled, computer-assisted, statement-based approach to data collection from texts) that seamlessly integrates close reading with computational modeling and transforms texts into rich syntactic-semantic data. The web-based open-source InkVisitor application, which enables creating and linking entities, supports furhter comprehensive analysis. Using the Bologna and Niort trials as illustrative examples, the authors showcase how this approach unveils and elucidates social, spatial, and discursive patterns within medieval dissidence, inquisition trials, and related records. Additionally, the paper delves into the future prospects of machine-produced CASTEMO, leveraging manually collected data as training data for enhanced outcomes.

In English

The paper outlines the methods employed in the DISSINET project for generating structured knowledge graphs from historical data, shedding light on the challenges faced by historians and researchers. The authors introduce Computer-Assisted Semantic Text Modelling (a human-controlled, computer-assisted, statement-based approach to data collection from texts) that seamlessly integrates close reading with computational modeling and transforms texts into rich syntactic-semantic data. The web-based open-source InkVisitor application, which enables creating and linking entities, supports furhter comprehensive analysis. Using the Bologna and Niort trials as illustrative examples, the authors showcase how this approach unveils and elucidates social, spatial, and discursive patterns within medieval dissidence, inquisition trials, and related records. Additionally, the paper delves into the future prospects of machine-produced CASTEMO, leveraging manually collected data as training data for enhanced outcomes.

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Links

101000442, interní kód MU

Name: Networks of Dissent: Computational Modelling of Dissident and Inquisitorial Cultures in Medieval Europe (Acronym: DISSINET) Investor: European Union, ERC (Excellent Science)