Graph Technologies in the Humanities 2020

The conference took place at the University of Vienna on 21 – 22 Feburary 2020 at Aula am Campus, Spitalgasse 2, Hof 1.11.

Friday, 21.02.2020


Welcome — Tara Andrews, University of Vienna

SESSION 1: Modeling the Scholarly Process

Chair: Tara Andrews, University of Vienna


Modeling as a Scholarly Process—The Impact of Modeling Decisions on Data-Driven Research Practices — Aline Deicke (Akademie der Wissenschaften und der Literatur, Mainz)


Towards Scholarly Modelling with Graph Technology. Data before Schema — Thomas Efer (Universität Leipzig)


Collaborative Modelling of Historical Information and Information Extraction from Sources — Francesco Beretta (Laboratoire de recherche historique Rhône-Alpes)


Möglichkeiten der Versionierung RDF-basierter Daten für den wissenschaftlichen (DH-)Diskurs — Martina Bürgermeister (Karl-Franzens-Universität Graz)



SESSION 2: Graphs and Language

Chair: Andreas Kuczera, Academy of Science and Literature, Mainz/Gießen


Testing Greek Grammar with Graphs—an Exploratory Approach to a Treebank — Nina Čengić, Neven Jovanović & Petra Matović (University of Zagreb)


Modelling the Semantic Relations Within Texts Using the UD NLP Tools for Syntactic Parsing, Neo4j Graph Database for Storing and igraph for Network Analysis Benedikt Perak (University of Rijeka)


Graph metrics as an alternative to statistical measures in linguistics — Anna Shadrova, (Humboldt-Universität Berlin)



SESSION 3: Short Presentations

Chair: Franziska Diehr, Prussian Heritage Foundation, Berlin


A Comparison of Graph and Collection-based Representations of Early Modern Biographical Archives — Meadhbh Healy, Thomas O’Connor & John Keating (Maynooth University)


Famine and Feast: The Problem of Sources for Linked Data Creation — Rainer Simon & Rebecca Kahn (Austrian Institute of Technology, Alexander von Humboldt Institut für Internet und Gesellschaft)


Working Collaboratively on Graphs — Marco Petris, (Universität Hamburg)


SPARQLing Ogham Stones: New options for analysing analog editions by digitisation in Wikidata — Sophie C. Schmidt & Florian Thiery (Universität zu Köln, Research Squirrel Engineers)


The Socinian Correspondence: A Graph-based Digital Scholarly Edition—Generic Tools and Approach —Patrick Toschka, Julian Jarosch, & Andreas Kuczera (Akademie der Wissenschaften und der Literatur, Mainz)


Using Graph Technologies to Interconnect and Enrich Public-Domain Music Resources — David M. Weigl & Werner Goebl (University of Music and Performing Arts, Vienna)


[KEYNOTE] Tracing our steps: new tools and new standards for academic transparency — Charlotte Roueché, professor emerita (King’s College London)


Buffet and Wine Reception

Saturday, 22.02.2020

SESSION 4: Graphs and the Arts

Chair: Joris van Zundert, Huygens Institute for the History of the Netherlands


Redoing Hairballs: Visualizing Complex Arts & Humanities Data in Multiple Spaces and Time — Florian Windhager, Saminu Salisu & Eva Mayr (Donau-Universität Krems)


Graphbasierte Wege durch Bilderzählungen. Eine digitale Edition von Giovanni Domenico Tiepolos Bildzyklus Divertimento per li Regazzi auf der Grundlage von CIDOC-CRM — Rostislav Tumanov, Gabriel Viehhauser, Alina Feldmann & Barbara Koller  (Universität Stuttgart)


Reimnetzwerke als Untersuchungsgegenstand der Mediävistik — Thomas Jäger, (pagina GmbH)



SESSION 5: Annotation and Networks

Chair: Thomas Efer, Leipzig University


Modelling the Unthought    — Iian Neill, Chiara Palladino, & Andreas Kuczera (Akademie der Wissenschaften und der Literatur, Mainz)


Graphtechnologien für die Analyse historischer Netzwerke mit heterogenen Datenbeständen — Elena Leitner, Julian Moreno-Schneider, Georg Rehm, Sina Menzel, Vivien Petras, Mark-Jan Bludau, & Marian Dörk (DFKI GmbH, Humboldt-Universität Berlin, Fachhochschule Potsdam)


Early Modern Korea’s Localized Yangban Assemblages in Neo4j — Javier Cha & Robin Wooyeong Na (Seoul National University) & Donghyeok Choi, KAIST


Using Graph Models for Annotating Vague and Uncertain Information — Cristina Vertan, Universität Hamburg


Final Discussion


End of Conference

The PDF-Programm can be found here.

Modelling the Scholarly Process

The idea of connecting our knowledge, or networking information, has been a desideratum of digital scholarship since its beginnings. As these networks lend themselves very easily to expression as graphs, our tools of choice have followed: many libraries, archives, museums, and other repositories of data in the humanities make their goods available via Linked Open Data, following the principles of the Semantic Web. Many other research projects in the humanities have adopted graph models and even graph databases as their base, as these lend themselves naturally to collections of information where the link, the connection, or the relationship is paramount.

The 2019 conference celebrated the breadth of application of graph-based approaches in the humanities. For 2020 we would like to follow up with a more focused theme, whose central question is: To what extent are our scholarly processes reflected in our data models, and how (if at all) do graph technologies allow us to capture these processes in the digital realm? That is to say, when we choose to link items of information together (say, the identification of a person in an inscription or of a place in a text), are we adequately capturing the process that led us to make this choice? What mechanisms are (or should be) available to enable fact checking and to reconstruct the provenance of information?

Key topics arising from the main theme Modelling the Scholarly Process  include:

  1. Provenance and source criticism – While Linked Open Data, in the form of RDF, has (outside of textual criticism) become something of a standard for repositories of humanities data, there is a certain hesitation around its reuse outside of the project in which it was created. The perception is that the RDF data will not be complete enough, accurate enough, or nuanced enough for use outside its original context. How can we address these questions of source critique, making the data maximally useful, either in an RDF-based project or in any other form of linked data?
  2. Validation – One of the points of resistance to the adoption of graph models more widely in the humanities is the lack of a validation framework as well-known as XML schemas. Insofar as validation is a necessary part of the scholarly process, how do we do this with graph models?
  3. Visualization – One of the acknowledged strengths of networked models is that they lend themselves very easily to visualization, which is a pivotal part of explaining the results and the scholarly process to our peers; at the same time, the “hairball” of a densely-connected network graph has become something of a cliché within the digital humanities. The urgent question thus arises: which visualization methods there are beyond the “hairball”? How can visualization techniques support the analytic richness of graph complexity while at the same time reduce it to the point of comprehensibility? Which modes are there to interact with graph data and how can visualization contribute to manipulation and editing of graph data?