Contextual Modelling in Digital Humanities

paper, specified "long paper"
  1. 1. Arianna Ciula

    University of Surrey Roehampton

  2. 2. Øyvind Eide

    Universität Passau

Work text
This plain text was ingested for the purpose of full-text search, not to preserve original formatting or readability. For the most complete copy, refer to the original conference program.

Contextual Modelling in Digital Humanities


University of Roehampton, UK


Universität Passau, Germany


Paul Arthur, University of Western Sidney

Locked Bag 1797
Penrith NSW 2751
Paul Arthur

Converted from a Word document



Long Paper


historical studies
data modeling and architecture including hypothesis-driven modeling
semantic analysis

Semiotic Framework

Kralemann and Lattmann (2013, 3399–400) claim that models should be understood in the semiotic theory put forward by Peirce, with three main classes of signs: symbols, icons, and indexes. They claim that models are icons because the dominant relation with the original objects they represent is one of similarity.
2 Different shades of iconic similarity between sign and object as theorized by Pierce correspond to three kinds of models in Kralemann and Lattmann:

Image-like models—for example, photographs where single qualities such as forms and shapes are seen as similar to the original objects.

• R
elational or
structural models—that is, diagrams where the ‘interdependence between the structure of the sign and the structure of the object’ (Kralemann and Lattmann, 2013, p. 3408) enables the modeller to make inferences about the original by manipulating its model

Metaphor-like models, which represent attributes of the original by a specific quality of their own not recognised by convention or habit.

Models do not act as signs in virtue of themselves. What establishes the model as a sign is the interpretative act of a subject, whether as creator or reader. The practical act of modelling connects the model to its interpretation, that is, to its specific semantic content in a given social and institutional context (Kralemann and Lattmann, 2013, 3402–3). The modeller’s judgement depends on his or her presuppositions connected to ‘theory, language. or cultural practice’ (Kralemann and Lattmann, 2013, 3417). It is evident that Kralemann and Lattmann reiterate the concept of models as middle ground between theory and objects.
4 The relationship of iconicity between the model and the object being modelled is partly externally determined (it relies on the similarity between the model and the object) and partly internal (it depends on theory, conventions, prior knowledge). Based on this duality they stress, on the one hand, the subjectively determined dependency of models on prior knowledge and theory and, on the other, their independence from these in light of the specific conditions of the objects being modelled.

Modelling in Digital Humanities

In this paper we take previous research (Ciula and Eide, 2014; Marras and Ciula, 2014) one step further by mapping Kralemann and Lattmann’s trichotomy of models as icons to digital modelling exercises in historical research. The prototypical cases we chose are a starting point to investigate how such models relate to the cultural objects they represent.

Figure 1. Models representing objects in context.
If we accept Kralemann and Lattmann’s argument it follows that by modelling we link models to qualities and relationships already existing in the objects being modelled. Such linking is based on choices that are made for a certain end, informing and motivating the act of modelling. Models are created in actual scholarly situations of production and use and cannot be seen as decontextualised operations. A model is partially arbitrary in that the same inferences drawn by manipulating one model could have been reached in other ways—for instance, using a different model.
In this framework, models operate as sign functions. To understand their epistemic role, we need to look at both how they come to be (context) and how the similarity relation with the object is realised. By analysing the association of syntactic attributes of the source object with the attributes of the model we focus indeed on the contextual elements. However, these need to be complemented with the analysis of the specific sign-function in which production and use of models is enacted, as indicated in Figure 1. Three examples will be used to analyse the three types of sign-functions in a DH context:

Image-like models. We will use an example from digital palaeography research (Ciula 2005; 2009), where the abstract model letter acts as an image-like model of the samples it was algorithmically generated from. What we can learn about the objects of analysis (the medieval handwritten letterforms) depends on the features being selected in the modelling process. However, the inferential power of the model is mainly based on a strong sensorial similarity between model and object.

Relational models. As an example we will use models of landscapes described in historical sources (Eide, 2013). The inferential power of the model relies on the common relational structure between object and model. What new we can know about the object of analysis very much depends on the correspondence between the structuring of the textual expressions in the modelling process and the structure of the map model.

Metaphor-like models.
5 We will use the example of network models used to capture information about references to persons in historical sources. These do not only tie specific textual passages to real-world historical entities, they also form parts of networks of co-references (Eide, 2009). The association of a fishing tool (the net) to describe relationships between people is metaphorical. The inferential power of the model leverages on a deep cognitive similarity between the model and the object. It can generate unexpected connections between the objects it represents, which exist ‘only’ metaphorically in a network.

Figure 2. Image-like model. Morphological features of segmented letter forms are modelled into an average morphing letter. Inferences on the manuscript handwriting are based on the analysis of the morphing letter-models in virtue of a ‘sensuous resemblance’.

Figure 3. Relational model. Relational textual expressions are modelled into geometrical relations. Inferences on space as expressed in the text are drawn in virtue of the corresponding spatial structure in the map.

Figure 4. Metaphor-like models. Person names and their relationships as referred to by a document are modelled, respectively, into entities (nodes) and into properties connecting them (links). Assertions of co-reference are also modelled into properties connecting entities. Thus the net is now used to model social relations as well as assertions about people.
Common for all three types of models is the inferential power operating at the interplay between their ‘intrinsic structure’ and their ‘extrinsic mapping’ (Kralemann and Lattmann 2013, 3409). Indeed, the features being selected in the modelling process are influenced by contextual elements of different kinds, including hypothesis, scholarly methods and conventions, sample selection, and the technologies being used. However, the epistemic power of the model relies more on extrinsic aspects (sensorial similarity) and less on intrinsic aspects in image-like models, while it depends very much on intrinsic aspects (cognitive similarity) and less so on extrinsic aspects in the case of metaphor-like models (Elleström, 2013).

In the paper we will focus on some aspects highlighted in Kralemann and Lattmann’s theory with respect to the role of
context in modelling acts and the nature of
representational relation between objects and models through practical examples. We believe that these two foci are where modelling practices in DH meet with this semiotic framework in productive ways.

We will address the questions outlined in the introduction by contextualising this framework with specific examples of image-like, relational, and metaphor-like modelling with respect to source-based digital humanities. Prior knowledge is a sine qua non to create models in the first place and to use them as interpretative tools with respect to the objects they are signs of (Ciula and Eide, 2014). The relationships between modelling processes and interpretative outcomes are neither mechanical nor directly causal; however, the type of similarity on which modelling relies shapes the interpretative affordances of those ‘anchor’ models. Modelling processes bring about investments and burdens with respect to our knowledge of the objects we model. In particular, models as signs relate to the interpretation of those objects in different ways, from the immediacy of visual similarity on the image end of the iconic continuum to the deep cognitive similarity on the metaphorical end.

1. By source-based modelling we mean the creation and use of models representing some kind of historical sources and cultural artefacts, such as documents and manuscript pages, together with any of their relevant components and their structure.
2. We do not dwell in this paper on the complexities around the cognitive concept of similarity.
3. The distinction between the three types of icons is not meant to be clear-cut. We follow Elleström (2013) in seeing these types as grades of a continuum rather than separate categories.
4. Past research concerned with the experimental or techno-sciences recognise models including computational models as mediators between theory and objects of analysis (e.g., Winsberg, 2003; Morrison, 2009). This is in line with sign-vehicles functioning as mediators between denotational and connotational qualities, between thing and meaning (MacEachren, 2004, p. 246).
5. In Kraleman and Lattman these models are claimed to be based on semiotic similarity, but this appears categorically misleading to us, so we privilege the concept of metaphor taken from Peirce.


Buzzetti, D. (2002). Digital Representation and the Text Model.
New Literary History,
33: 61–88.

Ciula, A. (2005). Digital Palaeography: Using the Digital Representation of Medieval Script to Support Palaeographic Analysis.
Digital Medievalist,

Ciula, A. (2009). The Palaeographical Method under the Light of a Digital Approach. In Rehbein M., Sahle, P. and Schaanm T. (eds),
Kodikologie und Paläographie im digitalen Zeitalter / Codicology and Palaeography in the Digital Age. Norderstedt: BoD, pp. 219–35,

Ciula, A. and Eide, Ø. (2014). Reflections on Cultural Heritage and Digital Humanities: Modelling in Practice and Theory, DATeCH ’14.
Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage, Madrid, Spain, May 2014, pp. 35–41,

Eide, Ø. (2009). Co-Reference: A New Method to Solve Old Problems,
Digital Humanities—Book of Abstracts, Maryland, pp. 101–3.

Eide, Ø. (2013). Why Maps Are Silent When Texts Can Speak: Detecting Media Differences through Conceptual Modelling, From Pole to Pole.
Proceedings of the 26th International Cartographic Conference, Dresden, August 2013,

Elleström, L. (2013). Spatiotemporal Aspects of Iconicity. In Elleström, L., Fischer, O. and Ljungberg, C. (eds),
Iconic Investigations. Amsterdam: John Benjamins, pp. 95–117.

Frigg, R. and Hartmann, S. (2012). Models in Science. In Zalta, E. N. (ed.),
The Stanford Encyclopedia of Philosophy, Fall 2012 ed. Stanford, CA: Stanford University,

Jannidis, F. and Flanders, J.
(eds). (2012). Knowledge Organization and Data Modeling in the Humanities: An Ongoing Conversation. Workshop at Brown University, Providence, RI, March 2012,

Kralemann, B. and Lattmann, C. (2013). Models as Icons: Modeling Models in the Semiotic Framework of Peirce’s Theory of Signs,
190(16): 3397–420.

MacEachren, A. M. (2004). How Maps Work: Representation, Visualization, and Design. Guilford, New York.

Mahr, B. (2009). Information Science and the Logic of Models.
Software and Systems Modeling,
8: 365–83.

Marras, C. and Ciula, A. (2014). Circling around Texts and Language: Towards ‘Pragmatic Modelling’ in Digital Humanities.
Digital Humanities—Book of Abstracts, Lausanne, Switzerland, July 2014, pp. 255–57.

McCarty, W. (2005).
Humanities Computing. Palgrave Macmillan, Basingstoke.

Morrison, M. (2009). Models, Measurement, and Computer Simulation: The Changing Face of Experimentation.
Philosophical Studies,
143(1): 33–57.

Winsberg, E. (2003). Simulated Experiments: Methodology for a Virtual World.
Philosophy of Science,
70: 105–25.

If this content appears in violation of your intellectual property rights, or you see errors or omissions, please reach out to Scott B. Weingart to discuss removing or amending the materials.

Conference Info


ADHO - 2015
"Global Digital Humanities"

Hosted at Western Sydney University

Sydney, Australia

June 29, 2015 - July 3, 2015

280 works by 609 authors indexed

Series: ADHO (10)

Organizers: ADHO