Linked Open Data for Literary History

Extracting, Modeling, Linking and Querying Data on the French Enlightenment Novel

Christof Schöch
(Trier University, Germany)

The International Conference for the Study of the Novel
Cluj-Napoca

21 Jun 2024

Introduction

• Hello and welcome everyone.
• It is great to be here!
• I am very honored by the opportunity to speak here.
  

Thanks

Prof. Adrian Tudurachi and the Institute of Linguistics and Literary History Sextil Pușcariu / Academy of the Romanian People’s Republic.

The Ministry for Research, Education and Culture of Rhineland-Palatinate, Germany, for funding this research (Mining and Modeling Text, 2019-2023).

Thanks to all the project contributors: Maria Hinzmann, Matthias Bremm, Tinghui Duan, Anne Klee, Johanna Konstanciak, Julia Röttgermann and many others.

Overview

• 1 – Introduction
• 2 – Linked Open Data for Literary History
• 3 – Mining: Information Retrieval
• 4 – Modeling: Linked Open Data
• 5 – Results: Networked Database
• 6 – Conclusion

• My topic today is a vision of Literary History that tries to be open, connected, meaningful and, of course, digital.
• To do so, I will show how what “Linked Open Data” is.
• Specifically, I will use the way we have used LOD in a project on creating a new way of engaging with literary history
• I hope it will be interesting to you, whether you are a literary historian, a scholar of history, or just curious about Digital Humanities.
• First, I will explain what I mean by linked open data.
• Then I will show how we used text mining to collect information on French literary history.
• And how we used LOD to model this data and link it together, as well as link it to other resources.
• At the end, I will show what kinds of issues can be addressed using the resulting data.

Linked Open Data for Literary History

Two Modes of Data (and Digital Humanities)

• Qualitative DH:
  • Datasets are typically small, curated, heavily annotated, flawless, specialized (‘smart data’)
  • Prototype: digital scholarly editions, e.g. Goethe, Faust Edition
• Quantitative DH:
  • Datasets are typically large, scraped, unannotated, with errors and biases, generic (‘big data’)
  • Prototype: Large Language Models trained on lots of text, e.g. BERT

• To provide a bit of context, let me start with a distinction between big and smart data, in Digital Humanities.
  

Reference: Schöch (2013).

Third Way: Bigger Smarter Data

• In my opinion, there is a third way, which I have described already quite some tim ago as “bigger smarter data”.
• It’s not just a compromise: somewhat bigger, but still reasonably clean data;
• It’s an attempt to bring scale (quantitative) to the details (qualitative),
  
• Primarily using two technologies:
  • text mining or machine learning (for information extraction)
  • and linked open data (for data modeling and querying)

Reference: Schöch (2013).

Background: What is Machine Learning?

• Fundamentally, ML involves detecting relations between features and labels
  • Features: simple properties that we can observe in texts (e.g.: word forms)
  • Labels: more complex phenomena that are relevant to our research (e.g.: direct speech or metaphors)
• We use this approach primarily for information retrieval
  • We start with a text collection
  • We may annotate part of the data with labels
  • Then train a new model, or use an existing model
  • Evaluate the performance of the model on the annotated data
  • And then derive labels from unannotated text

Background: What is Linked Open Data?

• How does “Linked Open Data” actually work?
• There are subjects, predicates and objects
• Subjects are entities such as authors or works
• Objects are other entities, but also topics, locations, etc.
• Predicates link subject and object, like a verb
• This results in very simple statements
• The potential of LOD only unfolds with a large number of simple statements
• This seemingly simple structure is very powerful, because it is so generic
• To illustrate this power, one could liken it to chess: simple rules with lots of possibilities
• All the information we identify, using text and data mining, from our texts, is expressed in this way.
• A knowledge graph is created, which becomes part of the so-called “semantic web”

Source: Wikidata.

Linked Open Data: Multilingualism

• In LOD, all information is modeled semantically
• That means, each “unit of meaning” as an identifier
• And it has a lable and a description
• More precisely: many lanels and descriptions, in many languages
• So LOD and Wikidata are inherently multilingual
• That’s a big asset in a global scholarly world

Background: What is Literary History?

• Goals of literary history
  • Collecting and documenting knowledge of literary history
  • Providing explanations for the development of literature
• Organizational principles
  • Nations, periods, movements/currents, genres
  • Authors and works: themes, forms, relationships
  • Similarities and differences, continuities and change

Literary History in Linked Open Data

• Building blocks
  • Subjects, including persons (authors, etc.) and works (primary texts, etc.)
  • Objects, e.g. themes, locations, protagonists, literary genre, etc.
  • Predicates, as required, including: author_of, about, influenced_by, etc.
    
  • Qualifications, e.g: Source (with type, date, URL)
• Some exemplary statement types
  • Bibliographic: [person] author_of [work]
  • Content-based: [work] about [theme]
  • Formal: [work] narrative_form [type]
  • Relations: [author] influenced_by [work]
  • and many more.

Our project’s key idea: Wikidata for Literary History

• Idea: Create a “Wikidata for the history of literature”
  • Literary history information system
  • LOD-based, with explorative interface and SPARQL endpoint
  • Approach of an “atomization” of the historical knowledge
  • Linking with other knowledge systems (taxonomies, standard data, knowledge bases)
  • Key values: human and machine readable, open, collaborative, multilingual
• Compared to Wikidata
  • Focused on one domain (French novel, 1750-1800)
  • Better coverage / higher density of information for this domain
  • Development of a systematic ontology
  • much smaller: 300k vs. 1.5 billion statements

• Digital: algorithmic methods; available online; readable by humans and machines
• Multilingual: through semantic modeling
• Collaborative: necessarily interdisciplinary; networked with other resources
• Open: freely available software; all data and resources freely available; also already in the process

The project ‘Mining and Modeling Text’

• Mining and Modeling text brought literary history, machine learning and linked open data together.
• Mining and Modeling was a project funded by the regional government from 2019 to 2023
• This was an interdisciplinary project involving computer science, literary studies, legal studies, computational linguistics, and digital humanities
• The key goal of this project was to use computational methods to build a knowledge base about the French novel of the Enlightenment
• But more generally, also, to experiment with the idea of “Linked Open Data for Literary History”
• The MiMoText project has pursued this goal of collecting literary-historical knowledge, making it machine-readable and networking it using LOD
• We use three different sources of information for this purpose
  • Bibliographic metadata; in particular the “Bibliographie du genre romanesque francais, 1751-1800”, by Martin, Mylne and Frautschi
  • Knowledge from literary history, especially overview chapters from handbooks
  • And characteristics of primary texts, in our case based on a corpus of 200 French novels from the period 1751-1800
• Work objectives
  • Automatically extract relevant information from these sources
  • Model this information as LOD and link them together as much as possible
  • Analyze this information to learn more about the literature of the period, but also about literary historiography.
  • This also means transforming heterogeneous sources into a homogeneous data set
  • It also means explicitly modeling everything, and this is where controlled vocabularies, taxonomies, ontologies and their implementation and use come into play.

More information: Schöch et al. (2022), Hinzmann et al. (2024)
and mimotext.uni-trier.de/en

Mining: Information Extraction

Pillar 1: Bibliographie du genre romanesque français

Reference: Martin, Mylne, and Frautschi (1977).

Pillar 2: primary literature (novels)

• Corpus of 200 French novels (1750-1800)
• Encoding: in XML-TEI, with metadata, according to ELTeC schema
• Methods of analysis: Topic modeling, NER, stylometry, etc.

Collection of Eighteenth-Century French novels (1750-1800), ed. Julia Röttgermann, github.com/MiMoText/roman18. See also Röttgermann (2024).

Pillar 3: Scholarly Literature

• Annotation guidelines => Manual annotations (using INCEpTION)
• Linking of INCEpTION with MiMoTextBase and Wikidata => disambiguation
• Creation of statements about authors and works (genres, themes, etc.)
• Machine Learning based on the annotated training data

Modeling: Data Modeling

Modular Data Model

• Module 1: Theme
• Module 2: Space
• Module 3: Narrative form
• Module 4: Literary work
• Module 5: Author
• Module 6: Mapping
• Module 7: Referencing
• Module 8: Versioning & publication
• Module 9: Terminology
• Module 10: Bibliography
• Module 11: Scholarly literature

Repository: github.com/MiMoText/ontology.

Example: The module on themes

Example: The module on narrative location

Meta-Statements

Linking with Wikidata for ‘federated queries’

Result: Networked Database

The MiMoTextBase

• Because all statements are available in our Wikibase instance, we can now also search and query this knowledge network
• I will first show a few examples from the explorative view, similar to a wiki.
• Author: Not a lot of information, but: “exact_match” (!) => Wikidata
• Works: much more information, both as short texts and formalized and standardized according to the data model
• This information comes from various sources, so there are also meta-statements
• You can also search here, but not in a very precise way => SPARQL:::

See: data.mimotext.uni-trier.de

SPARQL endpoint

• The Wikibase Query Service allows very flexible queries
• There are also a lot of visualization options that can be used directly without further development work
• Our Wikibase instance now contains well over 300,000 statements, so the possibilities are quite broad.
• However, you need to familiarize yourself with the query language.
• That’s why we also offer a tutorial and lots of sample queries:::

SPARQL = SPARQL Protocol and RDF Query Language
See: query.mimotext.uni-trier.de

MiMoText Base: Query for themes in novels

• Every piece of information we capture is identified using authority data
• When using Wikidata, this means labels and definitions are available in multiple languages
• For our own taxonomies, we have focused on French, German and English
• When performing queries, we can mobilise this multilingual potential
• The above is a query for the dominant themes in the 200 novels of our corpus;
• We can easily switch between languages for labeling the graph

Live SPARQL query: query.mimotext.uni-trier.de

Some sample queries: simple queries

• List of novels with information from BGRF
• Number of works per author (first 25)
• Themes of novels, in French and in English

Example queries: visualizations

• Number of novels per year
• Narrative forms over time (decades)
• Book history: print formats over time (5 years)
• Interactive network of statements around Micromégas

Sample queries: networked and federated

• Link with catalogue data from French National Library (using BNF id)
• Narrative locations of novels (map)
• Authors by birth year, with portrait)
• Alternative author names from Wikidata infobox
• Network of influences between authors (using ‘influenced by’)
• Querying MiMoText from Wikidata (it works both ways)
• Novels and basic information, from Wikidata

Sample queries: comparative queries

• Themes from topic modeling compared to themes in BGRF
• Themes from BGRF vs. Topic Modeling (in one query)

Conclusion

Opportunities & challenges

• Opportunities
  • Linking heterogeneous data from different types of sources
  • Modeling, collecting and comparing contradictory statements
    
  • Transparency in knowledge production (sources)
• Challenges
  • Lack of consensus on relevant statement types in the discipline
  • Complexity reduction (triple structure)
  • Interoperability (tension ‘Wikiverse’ vs. OWL standard)

Lessons Learned

• Federated queries
  • Central element of the LOD vision
  • => Making it happen is not trivial (data model, infrastructure)
• Modeling meta statements
  • Very important: perspectives / statements, not facts
    
  • => Very different approaches in different technical contexts
• Exchange across communities
  • Literary Studies vs. Digital Humanities vs. Wikiverse
  • => is essential but needs more development
• There is still so much to do!
  • => We are continuing this effort in a new project called
    ‘Linked Open Data in the Humanities’ (LODinG)

Many thanks for your kind attention
Vă mulțumim pentru atenție

Slides: https:/dhtrier.quarto.pub/cluj

Further resources

• Tutorial: https://docs.mimotext.uni-trier.de
• SPARQL endpoint: https://query.mimotext.uni-trier.de
• MiMoTextBase: https://data.mimotext.uni-trier.de
• MiMoText Ontology: https://github.com/MiMoText/ontology
• Reference publication: ‘Smart Modeling for Digital Literary History’
• Overview of visuals: mimotext.github.io/MiMoTextBase_Tutorial/visualizations.html

References

Hinzmann, Maria, Matthias Bremm, Tinghui Duan, Julia Konstanciak, Julia Röttgermann, Moritz Steffes, Christof Schöch, and Joëlle Weis. 2024. “Patterns in modeling and querying a knowledge graph for literary history [preprint].” In Pattern Theory in Language and Communication, edited by Sabine Arndt-Lappe, Milena Belosevic, Peter Maurer, Claudine Moulin, Achim Rettinger, and Sören Stumpf. Trier: TCLC. https://doi.org/10.5281/zenodo.12080340.

Martin, Angus, Vivienne G. Mylne, and Richard Frautschi. 1977. Bibliographie du genre romanesque français, 1751-1800. Mandell.

Röttgermann, Julia. 2024. “The Collection of Eighteenth-Century French Novels 1751-1800.” Journal of Open Humanities Data 10 (1): 31. https://doi.org/10.5334/johd.201.

Schöch, Christof. 2013. “Big? Smart? Clean? Messy? Data in the Digital Humanities.” Journal of Digital Humanities 2 (3): 1–19. https://journalofdigitalhumanities.org/2-3/big-smart-clean-messy-data-in-the-humanities/.

Schöch, Christof, Maria Hinzmann, Julia Röttgermann, Katharina Dietz, and Anne Klee. 2022. “Smart Modelling for Literary History.” International Journal of Humanities and Arts Computing 16 (1): 78–93. https://doi.org/10.3366/ijhac.2022.0278.