This paper introduces Codex, a digital environment for modeling the practice of scholarly annotation of textual information. Codex is a textas-graph solution that integrates a text-as-graph meta model via standof property annotations. Codex has three distinctive characteristics. First, all standof property annotations are connected to the user who created or edited them. Second, the aofrdances of standof annotation in Codex's real-time standof property text editor module (SPEEDy) make it convenient for users to overlay and overlap annotations, allowing for a multitude of interpretations to be captured without conlfict. 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' ontology toward an 'aspect-oriented' ontology of discrete traits or qualities, which produces a classification that is both finer and more capable 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 scholarly understanding of textual information. Using the Statement meta
model is crucial: it allows the modeling of recurring structural patterns of information by keeping their semantic diefrences intact, which in turn enables the researcher to keep the annotation model close to the research object and the associated research questions. Moreover, the lfexibility of the graph extends to modeling hierarchies and combinations 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’ information and highlights its potential for the humanities and textual research in general. 1
Our title stems from the observation that there are certain types of information 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 another interpretation that we have not anticipated – a yet unrealized possibility that we like to call the ‘unthought.’
In the digital humanities, we model information to create formalized
representations of non-formalized, non-structured data
Modeling, however, is also a research method that allows scholars to
investigate, 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”
One meta model frequently used in the digital humanities is the graph
While graphs oefr a suitable meta model according to which we can map
out our sources, graph databases provide a method for storing and
querying the resulting relationships and object classes. Consequently, graph
databases are rapidly gaining momentum among scholarly research
communities and industry professionals, as they support the representation of
multidimensional systems whose relationships are semantic in nature, and
therefore exhibit significant potential for modeling qualitatively complex
information
Finally, graphs ensure semantic flexibility: whereas the graph oefrs a generic abstract model for describing the data in the form of relational statements composed of nodes and edges, it does not provide any domainspecific vocabulary for describing classes of things and the nature of their relations. 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 previously unthought structures. 2
Codex is a web-based annotation environment that leverages the graph meta
model to support the modeling of scholarly annotation of textual
information. The foundational idea behind Codex is that information expressed
in linguistic form is constituted by structural patterns of various kinds
(spatial, narrative, etc.), and can be represented and investigated through the
1The 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
Codex uses standof properties to model textual annotations, based on
concepts developed by Desmond Schmidt
As standof 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 diefrent 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 diefring interpretations over the same text range without creating conlficting markups. Because the annotator’s user identifier in Codex is recorded on each standof property they create or modify, it becomes possible to separate and visualize interpretational layers by user. 2.3
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 suficient for representing a relationship between two nodes, more complex relationships involving more than two entities require a hyperedge, 2https://github.com/argimenes/standoff-properties-editor 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 temporal 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 relationship. 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 inherently 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
After transcribing Robert W. Carden’s edition of Michelangelo’s letters
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 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 suficient 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 annotations 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
Spatial narratives represent a perfect example of implicit knowledge
structures that we can model with Codex. The description of landscapes and
way-finding processes in textual narratives is, by definition, much less
structured than the cartographic representation of a region or itinerary through
an established grid of coordinates. Premodern spatial narratives, in
particular, are based on strongly non-cartographic cognitive processes, and
challenge our prevalently Cartesian perspectives in many ways
Spatial narratives, however, can be considered as knowledge systems with
their own concepts and paradigms, which are encoded through specific
linguistic and semantic tools
To clarify this idea, let us consider a passage from the Stadiasmus of the
Great Sea, the earliest portolan known to be in existence
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
pronominal structures (‘here,’ ‘which’), or common nouns defining locations
(‘town,’ ‘promontory’). The text is structured through a set of relations
connecting these entities: these are mainly distances (including expressions of
extent) and indications of orientation. Both of these categories appear to be
deceptively 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
context, etc.
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 semantically 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
In Codex, we use the Statement meta model to annotate spatial information
gathered from texts. A Statement usually takes the form of an event,
represented as a verb phrase with prepositional agents
Typically, spatial relations connect two features of the landscape (Place A and Place B) through diefrent 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. However, the model is almost never as simple as (From Place A to Place B) + (Distance/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 diefrent functional nodes than with simple subject-predicateobject 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 diefrent labels, depending on their function in a given context (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 Subject, and the destination acts as an Object. Additional concept nodes indicate 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 relationships 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 diefrent 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) cultural and environmental factors. We indicate these relationship types as direction_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
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 inlfuenced conceptual categorizations (‘shaped like a shield’). Although the relational nature of these statements is not empirical like in navigational relations, they can still be modeled as relational statements between nodes expressing places and concepts.
Figure 8 shows all the conceptual statements referring to the place Aspis in our passage, classified according to diefrent 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 indicates the nature of the relationship between place node and concept node. 4.3
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).
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 diefrent 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 relations; 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 oefrs a number of tools for representing this multi-dimensionality in a practical way. Standof properties allow individual users to alter and improve their annotations over time, and to capture any associated changes. The non-destructive nature of standof annotation, which does not change the text stream, greatly facilitates collaboration amongst scholars. Standof 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, standof is also used to anchor Event Statements to their textual origins, 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 lfexible, extensible, yet structured model for capturing the manifold morphosyntactical and semantic relationships encountered in complex texts. We believe that the combination of these tools gives researchers increased flexibility 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 standof properties and the multidimensional power of the graph, makes it possible 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. ec lo s e e g h n t a g le in h y ic r e u M q r o f e c a f r e t n I : 3 m i h o t s k l a t o l e g n a l e h c i Θ u ὸ yo π s Ἀ e ( d 4 ta . s p 0 n 4 o g
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