This ontology is aimed at representing a text variation graph.
For documentation see https://wolkenstein.github.io/tgo-ontology/.
Ontology versions:
- tgo_current.owl: current version, using namespace http://www.hinkelmanns.at/tgo/.
- tgo_0-1.owl: Versioned ontologie, using namespace http://www.hinkelmanns.at/tgo/0.1/.
A model of text as a variant graph can support representing genetic text editions. The proposed model makes it possible to describe the relations between tokens and their relative dependencies in text genesis. The main focus is on the representation of intradocumentary text revisions. Moreover the Text-Graph-Ontology enables the referencing of genetic text editions via the Semantic Web.
Text genetic editions are enjoying sustained popularity in the fields of scholarly editions and literary studies. Representatives of recent research projects are the Faust edition (Bohnenkamp, Henke, and Jannidis 2016) or the edition of the works of Arthur Schnitzler (Burch et al. 2016), both of which aim at a complete reproduction of the text genesis. These editions require the reconstruction of complex text genetic processes. Which sequence of tokens forms a specific text state? How can differences between versions be described? The extension of the model of the Text Encoding Initiative by elements required for genetic editions was the subject of a working group that presented its results in a draft (Burnard et al. 2010). Parts of this draft have been incorporated into the TEI Guidelines (TEI Consortium 2013). With TEI P5, complex genetic editions can be realized. However, the underlying structure of the hierarchical graph makes it difficult to reconstruct and compare text gradients, i.e. the evolutionary stages of a text with inline markup. It can of course be done using stand-off and out-of-line annotations, as James Cummings has pointed out (Cummings 2018, 13). This concept for a Text Graph Ontology does not aim to be the next paper criticizing the xml foundation of TEI P5 nor is it trying to compete with the interoperability of TEI encoded texts. The ontology is designed for the specific purpose of implementing a text variant graph using semantic web technologies for the encoding of genetic text editions.
Several project used graphs to represent textual variation in the recent years. They can be divided into methods based on markup and methods focusing on variant graphs. An approach to deal with the problem of overlapping annotations in XML is the proposed markup language ‘General Ordered-Descendant Directed Acyclic Graph’ (GODDAG: Sperberg-McQueen and Huitfeldt 2004, esp. 158). A similar model is the ‘Graph Annotation Format’ (GrAF: Ide and Suderman 2007) which extends the Linguistic Annotation Framework (LAF: Ide and Romary 2006). In GRaF an underlying text is segmented via stand-off nodes, which use the position of characters in the text as reference points. Edges link the annotations with text segments.
The problem of intradocumentary and intertextual variation is addressed by the ‘data structure for representing multi-version texts’ (Schmidt and Colomb 2009). Thy criticize the markup approach of models like GODDAG and GrAF (Schmidt and Colomb 2009, 499) and propose a variant graph where the edges contain the segments of texts shared by multiple variants.
Collating texts is the focus of the stemmaweb project (“The Stemmaweb Project” 2012–; Andrews and Mace 2013). Similar to Schmidt and Colomb 2009 a variation graph is used to represent versions of a text. The texts are segmented into tokens and form the nodes of the graph. Directed edges show the similarities and dissimilarities between individual text carriers. Undirected edges represent variant relationships like orthographic variation, grammatical variation, lexical variation etc. (Andrews and Mace 2013, 508).
Efer 2016 has comprehensively described the use of graph databases for the text-oriented Digital Humanities. He proposed ‘Kadmos’, a layered graph model for textual representation. His model includes the separation into types and tokens. The ‘Text as Graph’ model (TAG: Haentjens Dekker and Birnbaum 2017) stores the text in nodes of various length (Haentjens Dekker and Birnbaum 2017, §3).
This very brief overview of selected data models for textual representation has shown, that multiple models for the representation of texts as graphs exist. The proposed model of this paper is far from state of a stable model and aims to connect the idea of a variation graph with semantic web technologies.
The Semantic Web makes information accessible in a machine-readable way using standardized vocabularies and ontologies. A distinction is made between ‘nodes’, which can be objects or atomic values, and ‘edges’, which describe the relationship between nodes. A statement always consists of a triple ‘[subject] - [predicate] - [object]’. Semantic Web technologies enable easy annotating and linking the scholarly genetic edition other resources. The Text Graph Ontology uses the Web Ontology Language (OWL: W3C OWL Working Group 2012) to specify classes and properties.
The first problem which needs to be addressed is the segmentation of a text. There is no agreement between the briefly presented models on what the atomic unit of a text graph is. For the Text Graph Ontology tokens separated by white space should be assumed, with the possibility to extend the model on a sub token level. A diplomatic transcription and various normalization stages can be attached to a token as a string or generatet from a separated character graph.
The proposals for the annotation of text revisions of the Grazer Editionsphilogie are tailored to the needs of mediaeval editions. Hofmeister-Winter 2016 presents a categorization of text revision phenomena. She distinguishes between self-revisions, i.e. interventions in one's own text, and external revisions, which describe the interventions of another hand (Hofmeister-Winter 2016, 10). Self-revisions can be a direct component of text production (immediate revision) or take place at a later point in time (late revision). In their opinion, third-party revisions, on the other hand, take place exclusively in a later revision step as a late revision.
TGO uses an edge weighted directed acyclic graph to represent text. To enable the use of weight in a RDF-enviroment, the weighted edges are realised as nodes.
The basic graph of tokens consits of the Token, Start, End and Connector classes.
| Classes | Description |
|---|---|
| :Token | A 'Token' is a separated entity of a larger instance. |
| :Start | The start point of a graph of tokens. |
| :End | The end point of a graph of tokens. |
| :Connector | A 'Connector' represents a weighted connection between two nodes. |
To form the connection between to tokens, the properties from, to and weight are needed
| Object Properties | Description |
|---|---|
| :from | Domain ‘:Connector’; Range ‘:Start’, ‘:Token’ |
| :to | Domain ‘:Connector’; Range ‘:End’, ‘:Token’ |
| Data Property | Description |
|---|---|
| :weight | Domain ‘:Connector’; Range ‘xsd:int’ |
A stage of a text represents a defined path from start to end.
The following classes and properties are introduced:
| Classes | Description |
|---|---|
| :Stage | A 'Stage' represents a stage of an instance. |
| Object Properties | Description |
|---|---|
| :partOfStage | Domain ‘:Connector’; Range ‘:Stage’ |
Annotation groups consist of tokens.
| Classes | Description |
|---|---|
| :Group | A annotation ‘Group’ of Tokens. |
| Object Properties | Description |
|---|---|
| :partOfGroup | Domain ‘:Token’; Range ‘:Group’ |
As stated before, the segmentation of a text is an essential question for the design of the ontology. Therefore a specific class Text and a Word with corresponding WordConnectors is introduced to descripe a text more specific.
| Classes | Description |
|---|---|
| :Text | Basic node for a text. |
| :Word | A segmented token on word level. |
| :WordStart | Start node on word level. |
| :WordEnd | End node on word level. |
| :WordConnector | Connector on word level. |
| Object Properties | Description |
|---|---|
| :fromWord | Domain ‘:WordConnector’; Range ‘:Word’, ‘:WordStart’ |
| :toWord | Domain ‘:WordConnector’; Range ‘:Word’, ‘:WordEnd’ |
| :startOfText | Domain ‘:WordStart’; Range ‘:Text’ |
| :endOfText | Domain ‘:WordEnd’; Range ‘:Text’ |
The other segmenation available in TGO are Characters.
| Classes | Description |
| :Character | A segmented character including diacritics or abbreviations on token level |
| :CharacterStart | Start node on character level |
| :CharacterEnd | End node on character level |
| Object Properties | Description |
|---|---|
| :fromCharacter | Domain ‘:WordConnector’; Range ‘:Character’, ‘:CharacterStart’ |
| :toCharacter | Domain ‘:WordConnector’; Range ‘:Character’, ‘:CharacterEnd’ |
| :startOfWord | Domain ‘:CharacterStart’; Range ‘:Word’ |
| :endOfWord | Domain ‘:CharacterEnd’; Range ‘:Word’ |
For more details please read the documentation and/or contact the author.
Andrews, T. L., and C. Mace. 2013. “Beyond the Tree of Texts: Building an Empirical Model of Scribal Variation Through Graph Analysis of Texts and Stemmata.” International Journal of Human-Computer Studies 28 (4): 504–21. doi:10.1093/llc/fqt032.
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Cummings, James. 2018. “A World of Difference: Myths and Misconceptions About the TEI.” Digital Scholarship Humanities 6:i63. doi:10.1093/llc/fqy071.
Efer, Thomas. 2016. “Graphdatenbanken Für Die Textorientierten E-Humanities.” Dissertation, Universität Leipzig.
Haentjens Dekker, Ronald, and David J. Birnbaum. 2017. “It's More Than Just Overlap: Text as Graph.” In Proceedings of Balisage: The Markup Conference 2017, edited by B. T. Usdin, Deborah A. Lapeyre, James D. Mason, C. M. Sperberg-McQueen, and Norman Walsh. Balisage Series on Markup Technologies: Mulberry Technologies, Inc.Rockville, Maryland.
Hofmeister, Wernfried, Astrid Böhm, and Helmut W. Klug. 2016. “Die Deutschsprachigen Marginaltexte Der Grazer Handschrift UB, Ms. 781 Als Interdisziplinärer Prüfstein Explorativer Revisionsforschung Und Editionstechnik.” Editio 30 (1): 14–33. doi:10.1515/editio-2016-0002.
Hofmeister-Winter, Andrea. 2016. “Beredte Verbesserungen.” Editio 30 (1): 1–13. doi:10.1515/editio-2016-0001.
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TEI Consortium. 2013. “TEI P5: Guidelines for Electronic Text Encoding and Interchange.” Version 3.6.0. Accessed July 30, 2019. https://www.tei-c.org/Vault/P5/3.6.0/doc/tei-p5-doc/en/html/.
“The Stemmaweb Project: Tools and Techniques for Empirical Stemmatology.” 2012–. Accessed July 30, 2019. https://stemmaweb.net/.
W3C OWL Working Group. 2012. “OWL 2 Web Ontology Language: Document Overview (Second Edition).” W3C Recommendation 11 December 2012. Accessed June 08, 2018. https://www.w3.org/TR/owl2-overview/.