Bigger Smarter Data

Extracting, Modeling and Linking Data for Literary History

Christof Schöch
(Trier University, Germany)

Korea University
Seoul, South Korea

23 May 2024

Introduction

Thanks

Korea University, as well as KADH (Korean Association for Digital Humanities).

The Ministry for Research and Education in 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 – Bigger Smarter Data and Linked Open Data
• 3 – Mining: Information Retrieval
• 4 – Modeling: Linked Open Data
• 5 – Results: Queryable Database
• 6 – Conclusion

• Hello and welcome everyone.
• It is great to be here!
  
• My topic today is a vision of Digital Humanities that tries to be both bigger and smarter, without choosing between the two.
• To do so, I will show how what “Linked Open Data” is.
• And how LOD can be one way of achieving “bigger smarter data”
• 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 even, whether you are a literary historian, a scholar of history, or just curious about Digital Humanities.
• First, I will explain what I mean by “bigger smarter data” and 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 questions can be addressed using the resulting data.

Bigger Smarter Data:
Linked Open Data

Three 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. 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. ChatGPT

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 that we can observe in data
  • Labels, classes, or values that are relevant to our research
• We use this approach primarily for information retrieval
  • We start from a text collection
  • We may annotate part of the data, or use an existing model
  • And then derive labels, classes, values from the 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: https://www.w3.org/TR/rdf11-primer and Wikidata.

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
  • Similarities and differences, continuities and change
• Functions
  • Explanations for literary developments
  • 1. by cultural or socio-historical context
  • 2. by internal dynamics of the literary system

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) and mimotext.uni-trier.de

Literary History in Linked Open Data

• Building blocks
  • Subjects, including persons (author, etc.) and works (primary text, scholarly literature, etc.)
  • Objects, including works, but also themes, locations, protagonists, literary genre, etc.
  • Predicates, as required, including: author_of, about, sameAs etc.
    
  • Qualifications, e.g: Source (with type, date, URL)
• Some exemplary statement types
  • Bibliographic: [person] author_of [work]
  • Contentual: [work] about [theme]
  • Formal: [work] narrative_form [type]
  • and many more.

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

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)
• Coding: in XML-TEI, with metadata, according to ELTeC schema
• Analysis methods: Topic modeling, NER, stylometry, etc.

Collection of Eighteenth-Century French novels (1750-1800), ed. Julia Röttgermann. See also (Röttgermann 2024)

Pillar 3: Scholarly Literature

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

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: Queryable 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

• SPARQL = SPARQL Protocol and RDF Query Language
• Used to formulate queries

• 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:::

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)

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

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

Martin, Mylne, and 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.