An ontology for modelling the social, spatial, and semantic relations in pre-modern written sources: Takeaways from data model development in the Dissident Networks Project (DISSINET)

The extent of data collection in computational history is often delimited by the specific
hypotheses that drive the research in question. Such a parsimonious approach is completely
logical and in many cases sufficient; moreover, there is no such thing as “total” data
collection, because the data is to a degree in the eye of the beholder. At the same time,
however, historical research has a tried and tested tradition of more source-driven research,
where the close reading of sources often drives the direction of study more than the testing
of hypotheses. In this paper, we present our experience of developing a thorough data model
and user interface for the collection of structured data from medieval inquisitorial registers,
focusing mainly on the social, spatial, and semantic relations between historical actors,
groups, places, physical objects, concepts, and events. We undertook this as part of a
project that seeks to provide a networked perspective on religious dissent and its repression
in medieval Europe (Dissident Networks Project / DISSINET, https://dissinet.cz). In this
paper, we would like to discuss our data model and data collection practices as well as to
open the data model to suggestions on how it can be mapped upon existing standards in
order to enhance its interoperability.
From our experience, we derive several proposals which could be of interest to historians
who, on the continuous scale between hypothesis-driven and source-driven data collection,
lean somewhat more towards the latter. Our point of departure is that a data model for
source-driven data collection should allow as much relational complexity as the natural
language of our sources does. Our approach is not completely new from a conceptual or
technical point of view; it is based on statements composed of predicates and actants
(subjects and objects), and therefore close to the idea of “semantic triples”. However, we dig
quite deeply into the language of our sources to propose a way of recording its minutiae,
allowing for fuzziness and uncertainty, modifiers (e.g., adjectives, adverbs), temporal and
spatial relations (incl. relative chronology), modality (negative, question, possibility,
desirability etc.), and give specific meaning to the different actant positions (subject, objects)
of each verb for analytical purposes. We thus join the minority strand in current
computational history which departs from the idea of factoids, decided upon at the moment of
data collection, and rather translate the source into structured data quite extensively to later
adapt the specific data projections to the needs of particular research questions and
hypotheses we want to test.
This approach allows us to preserve the semantic structure and detail of the sources, while
also producing highly structured data suited to various kinds of computational analyses such
as social network analysis, socio-semantic network analysis, geospatial data analysis,
various regression models, etc. Data collection (or rather, production), in our view, thus
already amounts to computational modelling – i.e., in the first instance, we model the source
itself, preserving its original language, its vagaries, and its complexity, and only at a later
stage various research problems in our focus.
The talk does not focus so much on technical solutions (e.g., review of available data
collection environments) or standards. Rather, we explore conceptual issues and a practical
workflow that we believe can be inspirational for computational history more generally.
Nevertheless, the paper includes a brief demonstration of the inkVisitor software, a webbased open-source data collection environment currently under development in our project.