Digital Literary and Cultural Studies: State of the Art and Perspectives
10.13125/2039-6597/1392
Over the last decade Digital Humanities has ceased being a “niche discipline” and have become a major phenomenon in academic and cultural debate. Significant scientific results and outcomes have been achieved, and fundamental research infrastructures have been realized. Despite these far-reaching outcomes, Digital Humanities still does not have a satisfactory influence in the traditional disciplinary fields. We need to find new methods to deal with cultural artifacts and texts. Amongst the many emerging research fields in the DH, two come forth as the most promising and interesting: Big Data and distant reading; Semantic Web and Linked Open Data. In my paper I will argue that Big Data in the Humanities, although very promising, have some critical issues, and I will propose the idea of a Semantic Cultural and Literary Web, a collaborative infrastructure based on ontology driven semantic annotation of primary resources. 
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2039-6597
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Fabio Ciotti
Between
Modeling
Digital Literary and Cultural Studies: State of the Art and Perspectives
Text.Serial.Journal
https://ojs.unica.it/index.php/between
4
Between Literature and Digital Culture: Intersections and Contaminations
2015-01-15
2014-11-30
2015-01-15
1
Fabio Ciotti
2014
10.13125/2039-6597/1392
2039-6597
8
Modeling
en
Digital Literary and Cultural Studies: State of the Art and Perspectives
https://ojs.unica.it/index.php/between/article/download/1392/1183
4
   Digital literary and cultural studies:
   the state of the art and perspectives
                               Fabio Ciotti


      Literary Digital Humanities: the state of the art
     Over the last decade Digital Humanities has ceased being a “niche
discipline”, becoming instead a major phenomenon in academic and
cultural debates. According to numerous authorities, it represents one
of the few points of resistance in the general decline of the humanities:

        Digital Humanities represents a major expansion of the
     purview of the humanities, precisely because it brings the values,
     representational and interpretive practices, meaning-making
     strategies, complexities, and ambiguities of being human into
     every realm of experience and knowledge of the world. It is a
     global, trans-historical, and transmedia approach to knowledge
     and meaning-making. (Burdick, Drucker, Lunenfeld, Presner,
     Schnapp 2012: vii)

      The rapid spread of the term “Digital Humanities”, rather than
the more rigorous and older “Humanities Computing”, indicates this
success on a linguistic level, and shows the ambition of this vast and
all-encompassing field of study, whose internal borders within the
human sciences are becoming increasingly blurred.




Between, vol. IV, n. 8 (Novembre/November 2014)
Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives



     The Digital Humanities today has a significant presence in
university teaching1, research and cultural heritage institutions
(although their recognition in institutional contexts is far from
adequate, especially in Europe). The discipline has organizations at
national and international levels, which engage in scientific
cooperation through bringing together a large number of scholars,
organizing huge conferences and publishing authoritative monographs
and periodicals.
     In the past decades significant scientific results and outcomes
have been achieved, and fundamental research infrastructures have
been realized. These include:
     1) The now widely shared theoretical and methodological
awareness that the relationship between the humanities and the
computational methodologies is epistemologically and theoretically
relevant and not merely instrumental;
     2) The development of the concept of modeling as the intellectual
activity that characterizes the study of cultural objects and phenomena
in the digital ecosystem, mediating between the level of theory and
that of observation. Modeling is the method used in Digital
Humanities,. It is relatively theory-independent but requires the
formalization of theoretical entities and of the relationships between
these entities, as well as the operationalization of the procedures to link
those entities to observational data (ultimately to the textual and
linguistic material or factual context);
     3) The development of shared languages and standards for the
modeling, representation and dissemination of high quality digital
resources, which is an activity that comes out of strong cooperation
with the information sciences community. This includes Text Encoding




       For the European area see for instance the Digital Humanities Course
       1

Registry set up in the context of the DARIAH, the European research infra-
structure for the humanities at https://dariah.uni-koeln.de/.


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                                     Between, vol. IV, n. 8 (Novembre/November 2014)



Initiative (TEI) and related projects such as Epidoc, Encoded Archival
Description (EAD) and other important metadata standards2.
      4) The extensive digitization campaigns of primary and secondary
sources in textual and/or image facsimile formats, and the creation of
vast online repositories that provide free access to an important part of
the Western textual tradition.
      5) The development of software frameworks and infrastructures
for information retrieval, textual analysis, and online publication of
textual resources, typically available freely as open source products or
web services.
      Despite these far-reaching outcomes in the theoretical and
methodological rationales and in the development of general
infrastructure for research, Digital Humanities still does not have a
satisfactory influence in the individual traditional disciplinary fields.
As John Unsworth (2003) said more than 10 years ago:

        We need (we still need) to demonstrate the usefulness of all the
     stuff we have digitized over the last decade and more – and
     usefulness not just in the form of increased access, but specifically,
     in what we can do with the stuff once we get it: what new
     questions we could ask, what old ones we could answer.

     We could argue that the great influence of the post-structuralist or
neo-idealist approaches in literary and cultural studies plays an
important role in this distance between Mainstream and Digital
Humanities: “Theory” without adjectives, as defined by J. Culler
(1997), does not lend itself easily to interacting with the formalism of
data structures and computational models. But it is also true that the
computational methods for the analysis and the editing of texts and the


     2
       TEI is the most widely used standard for the digital representation of
textual data in the humanities (see http://www.tei-c.org), based on XML.
Epidoc is a TEI customization for editing epigraphical inscription. EAD is an
XML standard for the creation of digital finding aids in the archival context
(see http://www.loc.gov/ead/).


                                                                                  3
Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives



results in terms of critics and scholarly editions have often fallen short
of expectations, and where they have succeeded have rarely managed
to acquire sufficient recognition in the context of the traditional
disciplines.
     Despite increasing theoretical awareness, the tools of
representation and analysis produced so far have not satisfactorily
addressed the problem of the specificity and complexity of the cultural
and literary studies domain. In fact, the intellectual investment in the
definition of new models and languages for the formal representation
and processing of complex cultural objects has been rather low. Most
commonly we have inherited and applied models and languages
developed in computer science for different domains and necessities.
The case of XML is a good example of this. For many good reasons, it
has assumed a central role in the modeling of textual data. But it is well
known that XML requires the adoption of a tree like data model that is
not always suited to the structural nature of the objects to be
represented, and that is unable to adequately represent the numerous
and complex semantic levels that characterize a literary text (Ciotti
2011).

    Directions for the future: digital methods and tools
     Given this main picture, which directions should be taken in the
development of new digital methods and infrastructures for
humanities research? How can such efforts help to fill the gaps and
gain new insights into cultural and literary phenomenona?
     No doubt, to consolidate and extend the results already attained is
a mission to be pursued: text archives must be preserved and
extended; transcriptions and editions of primary sources using the
current formalisms must be promoted; standards must be maintained
and their wide application fostered. These are the basic requirements
and missions for a research infrastructure, such as the one promoted by
the recently established European consortium DARIAH (Digital
Research    Infrastructure     for    the    Arts    and    Humanities,
http://www.dariah.eu). However, can a research infrastructure provide


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the ability to enhance the overall level of research in its domain,
providing common innovative methodological tools and resources?
We acknowledge the fact that, contrarily to hard sciences or social
sciences, in the Humanities and in Literary Studies it is very hard to
find common methods. In fact, Humanities is the realm of
individuality in analysis and interpretation. Anyway, the digital turn
requires those methodological commons, and their implementation in
computational tools and services.
     Amongst the many emerging research fields in DH in this context,
two present themselves as the most promising and interesting 3:
     1) Big Data and distant reading: the application of text mining,
knowledge extraction or topic modeling algorithms and tools to the
Humanities digital data (whatever they mean).
     2) Semantic Web and Linked Open Data: the experimentation
with new formalisms and data models for semantic annotation in the
Literary and Cultural Heritage domain.

                  Big data and distant reading
     Big Data is the hype of the moment. The term refers to the
application of data mining and machine learning heuristics to search for
implicit recurring patterns and regular schemes inside wide amounts
of data (structured or not), usually not visible to the naked eye.
     The search for those patterns is based upon complex statistical
algorithms, the most known of which derive from bayesian probability
theory, where probability is the measure of the a priori plausibility
assigned to a state of knowledge or to a belief. When those algorithms
are applied to textual data the more specific term text mining is used. In
the DH context the most widespread method for textual corpora
analysis is topic modeling, that is the research of patterns of lexical




     As confirmed by the trend analysis of the subjects of the forthcoming
     3

DH 2015 conference papers conducted by Scott Weingart in his blog at
www.scottbot.net.


                                                                               5
Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives



tokens pattern co-occurring with a noticeable frequency inside a text or
a corpus (Jockers 2013, Underwood 2012)4.
      The groundbreaking steps in this direction are due to the
researchers of the Stanford Literary Lab, founded and directed by
Franco Moretti and Matthew Jocker. Moretti (2013) himself has
attempted to give a literary theoretical rationale to these experiments,
introducing the notion of “distant reading” (opposed to the traditional
“close reading” method in literary criticism as defined by New
Criticism). The basic idea of this approach is that there are synchronic
or diachronic literary and cultural facts that are undetectable to the
usual deep reading and local interpretation methods that require the
scrutiny of hundreds or thousands of texts and documents (and
millions of lexical tokens). In this way we can gain access to otherwise
unknowable information that plays a significant explanatory role in
understanding literary phenomena as the evolution of genres, the
affirmation of a style and its reception, and the presence of recurrent
content clusters in a given time span of literary history.
      I cannot go deeper into this issue here. However, I observe that
the enthusiasm showed by the now many practitioners of distant
reading and topic modeling seems to overshadow some critical issues.
      First, big data algorithms in general are completely independent
from the context (they can be applied indifferently to stock exchange
transactions or to very large textual corpora). They individuate
similarities and recurring patterns independently from the semantics
of the data. But in a sense, when you work with structured data
semantics is fixed a priori in the data schema; if you work with non or
low structured data (as is the case with large text only corpora), the
characters (or the n-grams) are the atomic data, and they play a very
limited semantic role. I am not saying that it is impossible to discover
interesting phenomena also at this level, but many relevant facts
concerning textual and literary objects are simply out of scope.


       Various tools implementing Latent Dirichlet Allocation (LDA), the
       4

most known variant of topic modeling algorithms, are available: Mallet,
Stanford Topic Modeling Toolbox, Serendip.


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     Texts, as any other cultural artifact, are essentially intentional
objects: to cite Daniel Dennet (1990), they are the products of the
intentional stance of their producers and users. The meaning of a word,
the usage of a metaphor, or the choice of a metric or rhythmic solution
in a poetic text, are determined by the attribution of sense and meaning
by the author and by the reader (I do not discuss here whether the
former is more or less relevant than the latter). They can be, and often
are, idiolectal or even unique. Purely quantitative and mass analysis
can delineate the textual “degree zero”, on which the secondary
modeling system of culture and literature builds its significance
(Lotman 1970).
     Moreover, although on a large scale quantitative methods can
give some insights into lexical meanings and their distribution, we
must observe that the meaning in literary texts is multi-layered, and
that some layers do not have direct lexicalization or have a very
complex and dispersed one (think about aspects of a narrative text at
different abstraction levels like anaphors, themes, plot and fabula,
actants).
     Besides these theoretical pitfalls, there are some methodological
and pragmatic ones as well. Firstly, to do use the Big Data methods
you must have Big Data. The dimension of the data sets used in hard
sciences, economics and (partially) social sciences are bigger by many
orders of magnitude than the largest textual datasets that we can have
in literary studies. The efficacy and adequacy of the probabilistic
algorithms in this context is not so certain.
     Secondly, if a so called very large textual set is composed of
documents spread over a long period of time, diachronic variation of
the form and usage of the language (both on the syntactic and semantic
levels) can invalidate purely quantitative and statistic measures.
     Finally, yet importantly, there is the problem of data quality and
of the assessment of the protocols followed to build the data sets, a
problem recently raised also in the context of hard sciences 5. If in social

     5
      See for examples what David Crotty (2014), senior editor at Oxford
University Press, observes in a recent blog post: “Detailed methodologies


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Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives



sciences a given level of statistical error in the data is “acceptable”, it is
hardly questionable that Humanities and Literary studies have a much
lower threshold.

     Semantic technologies and ontologies: towards Rich
                       (linked) Data
      Big Data methods rely on the application of quantitative
algorithms to large sets of simple and possibly unstructured data. The
semantic oriented approach is instead based on the modeling of
complex human interpretations of data through formal languages: we
can say that in this case we are creating and processing Rich Data. This
approach has similarities with the humanities tradition of annotation
and comment, and has informed the text encoding field in the Digital
Humanities.
      The first and most ambitious formulation of this idea in the
context of modern digital and networked technologies is the seminal
vision of the Semantic Web by Tim Berners-Lee in the late 90s (Berners-
Lee, Hendler, Lassila 2001). He proposed that the information resources
on the Web should be associated with a set of semantic metadata, so
that their intended semantics could be accessed and processed by
software agents.
      Semantic Web has become an official W3C initiative, which has
developed a number of languages and data models. The most basic one
it is Resource Description Framework (RDF)‫‏‬, a simple data model that
allows for binary predicates to be stated (subject – predicate – object).
RDF as such does not specify the content of those statements. That role

would be of tremendous value across the spectrum of scientific research. The
validity of many types of sociological studies, for example, depends greatly
on how those studies were conducted. Why not offer all the gory details to
better help readers understand whether the experiments were well conduct-
ed so we know whether the data is worth reusing? Beyond reproducibility,
increased availability of trusted protocols would be a boon to scientific pro-
gress simply because more people would have more access to more tech-
niques”.


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is reserved to the formal ontology level, using RDFS and OWL
formalisms (Antoniou, Van Harmelen, 2008; Di Noia, De Virgilio, Di
Sciascio, Donini, 2013).
     The original general vision of Semantic Web has proved to be
unfeasible for many technical and social reasons. Nonetheless,
Semantic Web methods and technologies have had successful
application in many restricted and controlled domains, and in the
context of the Linked Data.
     The term Linked Data (introduced again by Tim Berners-Lee)
refers to a set of guidelines for publishing and interlinking structured
data on the Web (Bizer, Heath 2011). These principles are the
following:
       1. Use URIs as names for things;
        2. Use HTTP URIs, so that people can look up those names;
        3. When someone looks up a URI, provide useful information,
           using the standards (RDF, SPARQL);
        4. Include links to other URIs, so that they can discover more
           things.
      Linked Data movement has grown quickly and Cultural Heritage
initiatives play a relevant role in this 6. My point is that a virtuous
convergence between cultural and literary digital resources, ontologies
and linked data practices represents a big opportunity for the future
development of Literary and Digital Humanities. Building this kind of
Rich Data for humanities research can also enhance the efficacy of text
mining technologies, and it must not be considered in contrast with
those tools and methods.




    6
     See the Web site of the “Linked Open Data in Library Archives and
Museum” (LODLAM) network, http://lodlam.net.



                                                                              9
Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives




  The case for ontology and why humanists should care
     In this overall picture I want to stress the centrality of formal
ontologies and of ontology building for the humanities.
     The term has been inherited from classic and medieval
metaphysics (since the Aristotelian system which denoted the theory of
being and its categories). Qualified by the adjective “formal”, it now
refers to the idea of giving a formalized account of a conceptual
description of (a portion of) the world:

          In the context of computer and information sciences, an
       ontology defines a set of representational primitives with
       which to model a domain of knowledge or discourse. The
       representational primitives are typically classes (or sets),
       attributes (or properties), and relationships (or relations
       among class members). The definitions of the
       representational primitives include information about their
       meaning and constraints on their logically consistent
       application. (Gruber 2009)

      The relevance of formal ontologies for literary and cultural objects
digital processing are both theoretical and operational.
      First, in the DH community a great relevance has been given to
the notion of the model and modeling. But the problem with the
model/modeling notions is that they are umbrella terms, relating to a
wide and diverse collection of conceptual objects and practices. In
general, we can sort the roles assigned to modeling in scientific activity
into three areas:
          representation/communication: models ensure that a
             community of practice shares the fundamental concepts of a
             domain;
          explanation/prediction: models relate facts and concepts
             providing explanations and possibly predictions of the
             behavior of a system;



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           multiple views/perspectives mediation: models mediate
             between the different perspectives that can arise within a
             single community of practice and between different but
             proximal communities of practice.
      Ontological modeling formalizes the common sense concept of
model, giving it a precise logical semantics and a definite functional
role in each of these areas.
      Creating formal models based on explicit conceptualization and
logical foundation assumes that all the discourses are firmly grounded
in a common “setting” of the domain.
      Formal ontologies permit the application of computational
inferences and reasoning methods to explain and to make predictions.
Their grounding in description logic has made possible the development
of efficient automatic reasoners and inference engines:

       Logic reasoning is one possible application for ontologies. It is
    probably helpful (i) to check consistency during ontology
    development, (ii) to enable semi-automatic merging of (domain)
    ontologies as well as (iii) to deduce hidden information contained
    in the ontology. (Zöllner-Weber 2009)

       Finally, Semantic Web modeling provides methods to compare
and eventually merge different ontologies and, being based on the
Open World Assumption, ensures the functionality of the model even
if it is incomplete or conceived as a work in progress.
       In the Humanities and Literary Studies, conceptual formalization
must face the deep problem of the indeterminacy of theories and of
theoretical terms. We can concede that indeterminacy is a characteristic
of the object domain. Nevertheless, as long as we want to use
computing we need to reduce that which is implicit and, with the
consciousness that formal modeling is inside the hermeneutic process
and that we are expected to modify and adapt it, must formalize it ad
infinitum. Nonetheless, at a given synchronic moment the model must
be determined, isomorphic to the domain and at the same time
dependent on the perspective of the community of practice who has


                                                                               11
Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives



responsibility for it. To recall Willard McCarty, an ontology is an
account of what the community knows, as much as it is an account of
how it knows what it knows.

  Toward an infrastructure for a Literary Semantic Web
     Given this theoretical context, I propose a sort of Literary and
Cultural Semantic Web, a digital environment and infrastructure
incorporating semantic methods and practices of digital interaction
and cooperation already available and tested in the Digital Humanities
community. The components of this networked infrastructure of
resources, tools and services are:
   1. large, high-quality document archives belonging to different lin-
      guistic traditions / cultures in standard encoding formats;
     2. a set of methods and computational tools for the distributed and
        cooperative annotation of digital resources;
     3. a set of domain specific shared ontologies to ground the annota-
        tions, organized in a multilayered way, each dedicated to a par-
        ticular aspect of the intratextual, extratextual and intertextual
        structure:
             ◦    real places and spaces chronologically adapted
             ◦    real people (including authors)
             ◦    works and literary history categories
             ◦    historical events
             ◦    fictional places and worlds
             ◦    fictional characters and entities
             ◦    themes and motives
             ◦    rhetorical figures
             ◦    genres and stylistic features
     4. tools able to visualize and process "semantic" levels of digital in-
        formation, which allow knowledge transfer and sharing within


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      the digital environment as linked data.
     Although these technologies and the relevant underlying
methodologies are now widely used, there are critical aspects to
highlight: formal ontology has the dual capacity of fixing prior
knowledge of what is in the domain, and, simultaneously, promoting
the development of new knowledge; multiple ontological analysis can
be connected with the same (passage of) text, creating multiple
knowledge and cultural content layers that overlap with the textual
layer, and thus uncovering its complexity; these stratified texts can be
re-used in different contexts, and by different kinds of users: from
“professional scholars” to culturally aware users who are attracted by
the potential text mash-ups. As a result, it is possible to use data for
processing and activities such as: visual representations of the texts'
content, the integration of text and maps, their re-use for tourist-
oriented services, etc.
     Building such an infrastructure is obviously a very demanding
task. But many of the building blocks are already there. Above all the
history and evolution of the Web has shown that it is possible to build
complex systems through a public, incremental and cooperative
process, and that this strategy proves to be much more efficient and
effective than private, monolithic and centralized ones.
     The infrastructure we are envisioning is intrinsically cooperative
and driven by crowd sourcing. This infrastructural model opens a
space into which “experts” - professional scholars - and “non-experts”
can enter, to read, visualize and analyze the resources at different
levels of complexity, and, in so doing, enrich them. The traditional
experts’ literary, aesthetic, historical-critical reading and interpretation
are no longer exclusive or dominant.
     Open data, collaborative annotation, ontologies, relations with the
context and connection to other network resources all contribute
towards defining a new form of digital “cultural literacy” (Hirsch
1987).




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Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives




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                            The author
Fabio Ciotti

      Fabio Ciotti is Assistant Professor at the University of Roma Tor
Vergata, where he teaches Digital Literary Studies and Theory of
Literature. He is President of the Associazione per l'Informatica
Umanistica e la Cultura Digitale (AIUCD, the Italian digital humanities
association); elected member in the TEI Consortium Technical Council
and in the EADH (European Association of Digital Humanities)
Executive Board.
      His scientific and research work covers various aspects and
themes of Digital Humanities and Literary Studies, both from the
theoretical and the practical point of view: the applications of
computational methods to the analysis of narrative texts; digital text
encoding and representation; applications of XML and TEI


                                                                             15
Fabio Ciotti, Digital literary and cultural studies: the state of the art and perspectives



technologies to literary computing; modeling and creation of digital
libraries; applications of new media and computer mediated
communication to Humanities research and teaching. Recently his
research interests concern the application of Semantic Web/Linked data
principles and technologies to humanities digital libraries and textual
corpora. He is interested in particular in ontologies for the semantic
analysis of literary texts and for the semantics of markup languages.
       Fabio Ciotti has been Chair of the Local Organization Committee
for TEI Conference and Members Meeting 2013; a member of the
Program Committee for the TEI Conference 2014 as well as for AIUCD
conferences. He has been scientific consultant for text encoding and
technological infrastructures in several digital libraries and archives
projects, most notably Biblioteca Italiana (Italian literary tradition,
http://www.bibliotecaitaliana.it) and DigilibLT (Late Latin tradition,
http://www.digiliblt.unipmn.it/). He is currently involved in the Geolat
project - aimed at building an ontology for the geographical
knowledge of the ancient world and funded by Fondazione San Paolo -
and in an Italian Ministry of University funded project (PRIN) for the
thematic annotation of Latin and early Italian texts.

         Email: fabio.ciotti@uniroma2.it



                                           The paper
Data invio: 30/08/2014
Data accettazione: 30/10/2014
Data pubblicazione: 30/11/2014



                              How to quote this paper
Ciotti, Fabio, “Digital literary and cultural studies: the state of the art
and perspectives”, Tecnologia, immaginazione e forme del narrare, Ed. L.



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                                Between, vol. IV, n. 8 (Novembre/November 2014)



Esposito,  E.  Piga,   A.      Ruggiero,,      Between,      IV.8     (2014),
http://www.Between-journal.it/




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