I2SC Lecture Series (Recording): Christof Schöch (Digital Humanities, University of Trier) Machine Learning and Linked Open Data for Literary History
Date: November 28, 2025
Abstract:
This talk presents an innovative approach to literary history that integrates machine learning (ML) with linked open data (LOD), demonstrating how computational methods can expand the scope of traditional humanities research. The approach was developed in the Mining and Modeling Text project at the Trier Center for Digital Humanities and leverages three complementary data sources: bibliographic metadata from the Bibliographie du genre romanesque français, a corpus of 200 XML-TEI encoded French novels (1750–1800), and secondary scholarship on the Enlightenment novel. Using ML techniques such as topic modeling and named entity recognition, we extract structured information and represent it as LOD triples in a public Wikibase instance. The resulting semantic knowledge graph enables complex querying across heterogeneous data, uncovering large-scale patterns and trends in 18th-century French literature. Beyond the case study, the talk highlights the broader potential of combining ML pipelines with LOD infrastructures to build open, federated, and multilingual knowledge resources for Computational Literary Studies and Digital Humanities more generally.
You can watch the recording of the talk below.
