=Paper=
{{Paper
|id=Vol-3110/paper8
|storemode=property
|title=Modeling the ‘Unthought’
|pdfUrl=https://ceur-ws.org/Vol-3110/paper8.pdf
|volume=Vol-3110
|authors=Chiara Palladino,Andreas Kuczera,Iian Neill
|dblpUrl=https://dblp.org/rec/conf/graph/PalladinoKN20
}}
==Modeling the ‘Unthought’==
https://ceur-ws.org/Vol-3110/paper8.pdf
Modeling the ‘Unthought’
Chiara Palladino1 Andreas Kuczera2
Iian Neill3
1 Furman University, Greenville, SC, USA
2 University of Applied Sciences (THM), Gießen, Germany
3 Academy of Sciences and Literature | Mainz, Germany
Abstract
This paper introduces Codex, a digital environment for modeling the
practice of scholarly annotation of textual information. Codex is a text-
as-graph solution that integrates a text-as-graph meta model via stan-
doff property annotations. Codex has three distinctive characteristics.
First, all standoff property annotations are connected to the user who
created or edited them. Second, the affordances of standoff annotation
in Codex’s real-time standoff property text editor module (SPEEDy)
make it convenient for users to overlay and overlap annotations, al-
lowing for a multitude of interpretations to be captured without con-
flict. Third, the Statement meta model enables the user to formulate
RDF-like assertions which can be applied to multiple entities. The
use of trait statements constitutes a move from a ‘class-based’ onto-
logy toward an ‘aspect-oriented’ ontology of discrete traits or qualit-
ies, which produces a classification that is both finer and more cap-
able of functioning as a doxography that connects ontological claims
to the historical sources from which they are derived. In this paper, we
demonstrate a number of practical applications for Codex, focusing on
how this solution contributes to the pragmatics of modeling the schol-
arly understanding of textual information. Using the Statement meta
Creative Commons License Attribution 4.0 International (CC BY 4.0).
In: Tara Andrews, Franziska Diehr, Thomas Efer, Andreas Kuczera and Joris van Zun-
dert (eds.): Graph Technologies in the Humanities - Proceedings 2020, published at
http://ceur-ws.org.
150
model is crucial: it allows the modeling of recurring structural patterns
of information by keeping their semantic differences intact, which in
turn enables the researcher to keep the annotation model close to the
research object and the associated research questions. Moreover, the
flexibility of the graph extends to modeling hierarchies and combina-
tions that may not have been thought of before, making it possible to
‘model the unthought.’ The final section concerns the usefulness of
this model for the understanding of unstructured ‘narrative’ informa-
tion and highlights its potential for the humanities and textual research
in general.
1 Introduction: Modeling the ‘Unthought’
Our title stems from the observation that there are certain types of inform-
ation that cannot easily be incorporated into hierarchical structures. This
information often originates from non-structured sources, such as textual
narratives, wherein multiple meanings are possible: there can always be an-
other interpretation that we have not anticipated – a yet unrealized possibil-
ity that we like to call the ‘unthought.’
In the digital humanities, we model information to create formalized rep-
resentations of non-formalized, non-structured data (McCarty, 2005). By
these means, we are able to make explicit structures of knowledge that are
usually left implicit in the sources being modeled. The formalization that
occurs as a result must be able to support computational reasoning in order
to ensure that machines can process and analyze the gathered information.
In this sense, computational manipulability is an indispensable prerequisite
for such models: they are useful for research precisely because they can be
queried, inspected, and visualized in several ways that provide new insights
into the material (Eide and Ore, 2019).
Modeling, however, is also a research method that allows scholars to in-
vestigate, interpret, and better understand the nature of a source through
a systematic approach. The transformation of the information encoded in
a textual/documentary medium into a logico-mathematical model implies a
high level of abstraction, necessitating the use of a “deep structure” (Buzzetti,
2002, p. 76) that reifies superficial structures and their reciprocal connec-
tions. For this reason, modeling processes that involve textual and qualitat-
ive data generally rely on meta models, which represent abstract knowledge
structures that support the scholarly process.
One meta model frequently used in the digital humanities is the graph
151
(Flanders and Jannidis, 2019).1 Graphs represent knowledge in the form of
relationships (also named edges) between entities (also named nodes), and
are particularly appropriate for the representation of strongly networked in-
formation. At the core of this modeling approach is the idea that knowledge
can be represented as a hypergraph, wherein classes of things are reciproc-
ally connected by semantically meaningful relationships. The graph meta-
phor is also useful for representing the multi-dimensionality of textual data
and the variety of its accompanying interpretations. Here the idea of multi-
dimensional connectivity does not necessarily imply that we think of a know-
ledge domain visually as a graph, but rather offers us a way to map and model
the knowledge that it expresses in a sophisticated way by emphasizing and
empowering its relational nature.
While graphs offer a suitable meta model according to which we can map
out our sources, graph databases provide a method for storing and query-
ing the resulting relationships and object classes. Consequently, graph data-
bases are rapidly gaining momentum among scholarly research communit-
ies and industry professionals, as they support the representation of multi-
dimensional systems whose relationships are semantic in nature, and there-
fore exhibit significant potential for modeling qualitatively complex inform-
ation (Robinson et al., 2013).
Finally, graphs ensure semantic flexibility: whereas the graph offers a
generic abstract model for describing the data in the form of relational
statements composed of nodes and edges, it does not provide any domain-
specific vocabulary for describing classes of things and the nature of their re-
lations. Vocabularies are usually provided by external schemas or ontologies,
although they can also be built by the scholar who is modeling the source.
In other words, they allow the modeling of new interpretations and of pre-
viously unthought structures.
2 Codex
Codex is a web-based annotation environment that leverages the graph meta
model to support the modeling of scholarly annotation of textual inform-
ation. The foundational idea behind Codex is that information expressed
in linguistic form is constituted by structural patterns of various kinds (spa-
tial, narrative, etc.), and can be represented and investigated through the
1
The idea of using graphs to represent humanities data is not new. It quickly gained
a foothold in social network analysis and literary studies, in the wake of Franco Moretti’s
groudbreaking work (Moretti and Piazza, 2007; Moretti, 2011), but graphs have also seen
use as a meta model to investigate and represent a wide variety of other knowledge structures,
including neural networks, street maps, textual transmission, GIS and spatial modeling, and
even as a new paradigm to model phenomena in ancient history (Malkin, 2011).
152
paradigm of multidimensional graphs.
2.1 Standoff Properties
Codex uses standoff properties to model textual annotations, based on con-
cepts developed by Desmond Schmidt (Schmidt and Colomb, 2009). Stan-
doff properties were chosen primarily for the simplicity they offer in record-
ing freely overlapping annotations. They differ from markup languages like
XML in that they are stored externally to the text and need not conform to a
context-free grammar.2 This enables the annotator to mix annotation types
from different vocabularies, combining, for example, TEI codes with those
of other domains, such as syntax, stylistics, custom semantics, etc. In addi-
tion, standoff is used to represent not only semantic encoding, but also style,
presentation, and layout. A special tool called the Standoff Property Editor
(SPEEDy) was devised for Codex to enable the user to mark up documents
with standoff annotation and modify the text stream freely without invalid-
ating the standoff property character positions. SPEEDy also interfaces with
the Codex API to create entities in the Neo4j graph database corresponding
to the semantic annotations added to the document.
2.2 Annotation Layers
As standoff properties are exogenous to the text stream and reference text
ranges by character position indexes, one may think of properties which
overlap the same text range as being layers of interpretation – provided, of
course, that we regard annotation as an act of interpretation. These layers
can be used to capture different perspectives on annotation which otherwise
might be excluded from an inline markup process, either as a result of the
definitions of the XML schema employed, or due to the technically complex
nature of representing overlap in XML. For example, if two editors disagreed
about the place signified by the deictic ‘there’ in a text, they could annotate
their differing interpretations over the same text range without creating con-
flicting markups. Because the annotator’s user identifier in Codex is recor-
ded on each standoff property they create or modify, it becomes possible to
separate and visualize interpretational layers by user.
2.3 Statements
A Statement in Codex is a data structure that enables the annotator to define
a relationship between two or more entities within a given context. While an
edge is sufficient for representing a relationship between two nodes, more
complex relationships involving more than two entities require a hyperedge,
2
https://github.com/argimenes/standoff-properties-editor
153
that is, an edge in a hypergraph model that connects at least two nodes. An
example of an edge is a relationship like subset of, which is between two nodes
only, whereas an example of a hyperedge in Codex is the received relationship
which can involve a sender, a receiver, an object, a destination, etc. When
this Statement hyperedge relates to an event or action, it is called an Event
Statement – the assumption being that an event can be considered to be a
kind of relationship. An event can be thought of as a relationship in its tem-
poral context. For example, we might say that Andrea del Verrocchio taught
Leonardo da Vinci and is thus connected to the latter by the teacher of re-
lationship. However, this relationship is in itself a byproduct of a series of
‘teaching actions,’ as a result of which consensus at some point bestows the
status teacher of. In Codex, the model that can be used to represent such
‘teaching actions’ is the Event Statement. The term ‘statement’ was chosen
above a term like ‘claim’ as the latter can be taken to suggest (at the least)
that the author intended a remark to be in some way ‘factual’; whereas a
‘statement’ can be thought of as merely a formulation which is not inher-
ently true or false. In this way, Codex contains a collection of hundreds of
Event Statements drawn from remarks in primary source documents rather
than ‘claims’ – which may be true or false – and thus leaves the evaluation of
veracity to the annotator or indeed to the reader (or rather, the user) of the
text corpus.
3 An Example of Modeling the ‘Unthought’
After transcribing Robert W. Carden’s edition of Michelangelo’s letters
(Carden, 1913) in Codex, Iian Neill applied various annotation layers such
as ‘persons,’ ‘places, ‘concepts,’ ‘traits,’ ‘sentiment,’ parts of speech, and
morphosyntactics to the texts in question. Figure 1 shows one of the let-
ters with highlighted sentiment and syntax analysis. All of these different
annotation layers are combined in the graph model. In Figure 2, we see the
entity tab of Codex, which displays all annotated entities in the letter, includ-
ing pronouns. These pronouns are annotated in a semi-automatic way, with
Google syntax analysis nominating a pronoun as a candidate for annotation
and the user then assigning an entity reference. Once the pronoun has been
annotated, the reference can be copied across to other pronoun co-references
in the document, thus combining manual and automatic annotation meth-
ods. In this manner, all references to an entity in a given document are cap-
tured, whether by name or by pronoun.
Two major advantages of this method are that one can find every reference
to an entity in the graph even if that entity is not directly named, and that the
accuracy of entity statistics in a text corpus is significantly increased. To take
154
a random example from the Codex edition of the Michelangelo Letters, in
the letter from Michelangelo to his brother Buonarroto dated 5 September
1512, Michelangelo’s name appears only once, in the letter’s signature. But
when all of the pronouns had been annotated, it became clear that there were
14 references to Michelangelo in total, which in turn constitutes 34% of all
the references to various people found in the letter.
Bearing these capabilities of overlapping syntax and semantics in mind, we
asked ourselves if we could find a sentence in the corpus where Michelangelo
talks about himself in the second person. Figure 3 shows the interface for
this query, and Figure 4 presents the query results. Two such sentences were
found, both occurring in a letter in which Michelangelo remonstrates with
his brothers, Buonarroto and Giovansimone:
[I]f thou hadst loved me in very truth thou wouldst have written to me
after this manner: ‘Michelagniolo, spend the three thousand crowns on
yourself in Rome, for you have given us so much that we already have
sufficient for our needs. We have more care for your person than for
your property.’
Thanks to the coexistence of syntax and semantic layers in one system, it
becomes possible to solve this exemplary research problem in the matter of
a few seconds. However, when we originally added semantic and syntax an-
notations to the corpus, we did not set out to answer questions like this – the
ability to query for overlapping annotations led us to this discovery only after
the fact. As it turned out, we had inadvertently ‘modeled the unthought.’
4 Ancient Spatial Narratives in Codex
Spatial narratives represent a perfect example of implicit knowledge struc-
tures that we can model with Codex. The description of landscapes and
way-finding processes in textual narratives is, by definition, much less struc-
tured than the cartographic representation of a region or itinerary through
an established grid of coordinates. Premodern spatial narratives, in partic-
ular, are based on strongly non-cartographic cognitive processes, and chal-
lenge our prevalently Cartesian perspectives in many ways (Palladino, 2016;
Barker et al., 2016).
Spatial narratives, however, can be considered as knowledge systems with
their own concepts and paradigms, which are encoded through specific lin-
guistic and semantic tools (Thiering and Geus, 2014). We can define these
tools as regular expressive patterns describing different types of functional
information related to space and navigation. In this sense, ‘modeling the un-
thought’ means to make explicit thought processes that are largely implicit
155
in premodern navigational systems – whether it be the semantic typologies
of these patterns, the different kinds of spatial relations they express, or the
formal-linguistic constructs used to convey this information.
To clarify this idea, let us consider a passage from the Stadiasmus of the
Great Sea, the earliest portolan known to be in existence (Arnaud, 2017):
114. Ἀπὸ Λέπτεως εἰς Θερμὰς στάδιοι ξʹ· κώμη ἐστί· τὸν δʼ αὐτὸν τρόπον
καὶ ὧδε βράχη ἐστὶ δυσκατάγωγα. 115. Ἀπὸ Θερμῶν πλεύσας σταδίους μʹ
ὄψει ἀκρωτήριον ἐπʼ αὐτῷ ἔχον δύο νησία ἐσκολοπισμένα· ὕψορμός ἐστιν.
116. Ἀπὸ τοῦ ἀκρωτηρίου ὄψει Ἀδραμύτην τὴν πόλιν ἀπὸ σταδίων μʹ·
ἀλίμενος. 117. Ἀπὸ Ἀδραμύτου ἐπὶ τὴν Ἀσπίδα στάδιοι φʹ· ἀκρωτήριόν
ἐστιν ὑψηλὸν καὶ περιφανές, οἷον ἀσπίς. Ἐπʼ αὐτὴν πλέε τὴν ἄρκτον [ὡς]
παραφαίνειν ἐξ εὐωνύμων.
114. From Leptis to Thermae sail for 60 stades; it is a town, and here
in the same way the shoals make the sailing difficult. 115. From Ther-
mae sailing 40 stades you will see the promontory against which are two
islands staked out with pilings; there is an anchorage. 116. From the
promontory you will see the city of Adramyte, distant 40 stades: it does
not have a harbor. 117. From Adramyte to Aspis 500 stades; it is a
high and conspicuous promontory, shaped like a shield. From there sail
north [so that] it appears on the left.
Some of the key concepts of this passage are relatively easy to isolate. First,
the core of any spatial description is an open class of spatial entities, which
include place names (Aspis, Thermae), as well as anaphoric, deictic, and pro-
nominal structures (‘here,’ ‘which’), or common nouns defining locations
(‘town,’ ‘promontory’). The text is structured through a set of relations con-
necting these entities: these are mainly distances (including expressions of ex-
tent) and indications of orientation. Both of these categories appear to be de-
ceptively simple to classify, when in fact distances can be expressed in a wide
range of formats, with enormous variations that depend on several factors,
including the mode of transportation, the season, the type of route, the con-
text, etc. (Palladino, 2016). Expressions of orientation, on the other hand,
seem to follow two main reference frameworks: they can function through
absolute reference points, such as the compass rose or the movement of the
sun (‘sail north’), or through relative reference points (‘against which,’ ‘on
the left’). Their expressive structures, however, may vary widely, and rep-
resent one of the most difficult aspects of spatial descriptions to model in a
controlled framework. Equally important are the semantic categories that
are projected onto spatial entities, which can be expressed as place types
156
(‘the city of Adramyte’), various kinds of qualities (‘a high and conspicuous
promontory,’ ‘there is an anchorage’) or even abstract concepts (‘shaped like
a shield’). Finally, a scholar may be interested in including some real-world
information through the association of entities to concrete locations.
Spatial narrative is a multi-dimensional system, where multiple layers of
information interact with each other to create meaning, which is precisely
what makes the graph structure so well-suited for modeling this kind of se-
mantically complex network. In the following sections, we will show how it
is possible to annotate and model this information using the graph structure
provided by Codex.
4.1 Modeling Spatial Relations with the Statement Meta Model
In Codex, we use the Statement meta model to annotate spatial information
gathered from texts. A Statement usually takes the form of an event, repres-
ented as a verb phrase with prepositional agents (Neill and Kuczera, 2019).
The idea is that any information in a text can be modeled as a semantically
classified set of relations between different kinds of entities: the entities are
represented as nodes fulfilling a specific function in the relation (e.g. as sub-
ject or object), and the Statement can be associated to a semantic label ex-
pressing its function (e.g. a distance).
Typically, spatial relations connect two features of the landscape (Place A
and Place B) through different types of functional linking, such as distances
or directions. Distances can be expressed as connection between two places:
the place from which the distance is calculated and the destination. How-
ever, the model is almost never as simple as (From Place A to Place B) + (Dis-
tance/Direction). Additional semantic information is contributed by the
value of the distance (when present) and the unit expressing it. Moreover,
grammatical structures, like verbal or adverbial constructs, can contribute
important information, e.g. the transportation mode (‘sailing,’ ‘walking’),
the type of position (‘to lie,’ ‘to be adjacent’), and so on. In other words,
spatial relations in texts have more in common with hyperedge structures
that connect different functional nodes than with simple subject-predicate-
object triples (see above).
Figure 5 shows how a relation of the type ‘distance’ can be modeled in the
graph structure of Codex. The sentence, as it appears in the text, is modeled
as a :Statement node, or Event, and the participating entities are linked to it as
agent nodes with different labels, depending on their function in a given con-
text (e.g. :Place). Relationships between the nodes are semantically labeled
in the graph structure according to the role of each node: for example, in a
distance relation, the place from which the distance is calculated acts as a Sub-
157
ject, and the destination acts as an Object. Additional concept nodes indic-
ate other information, such as the distance quantification and measurement
unit (‘40 stades’) and the mode of transport (‘sailing’). The entire statement
is then classified through a :Relation-type node, which is labeled according to
an external or user-defined vocabulary. Figure 6 shows how these relation-
ships appear in the graph database, where they can be queried by the scholar
for further investigation.
Directions work in a similar way, with the additional issue that they tend
to follow different reference systems, since they can use a variety of reference
points as landmarks for navigation. The choice of such features depends
on a variety of (potentially subjective and at least partly unpredictable) cul-
tural and environmental factors. We indicate these relationship types as dir-
ection_absolute and direction_relative, with further specifications added if
and when available. Figure 7 shows examples of how directional relations
appear in the Codex graph database.
4.2 Use of the Statement for Conceptual Relations
The projection of cultural or conceptual categories on spatial entities can
take the form of simple place-type declarations (‘it is a town’), indications
of geographical characteristics (‘it does not have a harbor’), or culturally in-
fluenced conceptual categorizations (‘shaped like a shield’). Although the
relational nature of these statements is not empirical like in navigational re-
lations, they can still be modeled as relational statements between nodes ex-
pressing places and concepts.
Figure 8 shows all the conceptual statements referring to the place Aspis in
our passage, classified according to different semantic types. The structure
of the statement is similar to distances and directions: the subject node is the
place to which the concept is associated (Aspis), while the node expressing
the :Concept is modeled as the statement name. Meanwhile, a :Type node is
associated to the entire statement, functioning as a semantic label that indic-
ates the nature of the relationship between place node and concept node.
4.3 External Properties
Finally, external properties are used to add further information concerning
specific locations in order to improve data mining and support map-based
visualizations of the network. External properties in Codex follow the usual
key-value format of graph databases (the property latitude, for example, has
the value ‘36.837800,’ as can be seen in Figure 10).
158
5 Conclusion
In the previous sections of this paper, we have shown how Codex and the
underlying graph model can be used to annotate and investigate a diverse
range of data, including grammatical, expressive, and spatial information.
In a machine-actionable model, this information typically constitutes differ-
ent layers of data. In the original non-digital source, however, every part
of every layer overlaps and interacts: grammatical information appears in
expressions of sentiment; external coordinates frame places in spatial rela-
tions; pronouns overlap with semantic information; and so on. Thanks to
the graph model, it is possible to represent this overlapping structure and
its interactions. In other words, Codex offers a number of tools for repres-
enting this multi-dimensionality in a practical way. Standoff properties al-
low individual users to alter and improve their annotations over time, and
to capture any associated changes. The non-destructive nature of standoff
annotation, which does not change the text stream, greatly facilitates collab-
oration amongst scholars. Standoff annotation also enables the free mixing
of annotation schemata, which are normally constrained by the hierarchical
grammars of XML documents. One can combine (or overlay) annotation
types from TEI-XML, corpus linguistics, syntax analysis, and so on. What
is more, standoff is also used to anchor Event Statements to their textual ori-
gins, thereby bringing the graph model and the text stream into a kind of
interoperability which allows the user to pass from text to graph model and
vice versa. As we have demonstrated in this paper, the Event Statement is a
flexible, extensible, yet structured model for capturing the manifold morpho-
syntactical and semantic relationships encountered in complex texts. We be-
lieve that the combination of these tools gives researchers increased flexib-
ility in modeling their scholarly process, and therefore a way to bring the
‘Unthought’ in their research to the surface. Scholars are often confronted
with a situation where they cannot store every piece of information that they
come across in their research process. Codex, with its combination of stan-
doff properties and the multidimensional power of the graph, makes it pos-
sible to freely annotate texts and combine various annotation hierarchies and
modeling perspectives. In this way, the full range of knowledge gathered
during the research process can be preserved and made accessible to other
researchers and research communities.
159
Figures
160
Figure 1: Segment of text with various annotation layers
161
Figure 2: List of entity statistics from a single document
162
Figure 3: Interface for querying the second person of the entity
Michelangelo
163
Figure 4: Sentences in which Michelangelo talks to himself in the second person
164
Figure 5: An example of a distance relation from the text cited on p. 4 (Ἀπὸ Θερμῶν πλεύσας σταδίους μʹ ὄψει ἀκρωτήριον), expressed in the graph
meta model. The sentence translates as “From Thermae sailing 40 stades you will see the promontory.”
165
Figure 6: Example as a statement in the graph database of Codex. Note that even the source itself can be modeled as an additional node.
166
Figure 7: Some examples of directions as they appear in the graph database, including sentences like: “the promontory appears on the left,”
“From there (Aspis) sail north,” and “against which (the promontory) are islands.”
167
Figure 8: The concepts associated to Aspis in our example. Aspis is associated with the image of a shield, the qualities of high visibility and
elevation, and finally with the place type of a promontory.
168
Figure 9: The same data represented as a graph meta model
169
Figure 10: External properties associated with Aspis in the Codex graph database
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