=Paper=
{{Paper
|id=Vol-2365/05-TwinTalks-DHN2019_paper_5
|storemode=property
|title=It Takes a Village: Co-developing VedaWeb, a Digital
Research Platform for Old Indo-Aryan Texts
|pdfUrl=https://ceur-ws.org/Vol-2365/05-TwinTalks-DHN2019_paper_5.pdf
|volume=Vol-2365
|authors=Börge Kiss,Daniel Kölligan,Francisco Mondaca,Claes Neuefeind,Uta Reinöhl,Patrick Sahle
|dblpUrl=https://dblp.org/rec/conf/dhn/KissKMNRS19
}}
==It Takes a Village: Co-developing VedaWeb, a Digital
Research Platform for Old Indo-Aryan Texts==
https://ceur-ws.org/Vol-2365/05-TwinTalks-DHN2019_paper_5.pdf
It Takes a Village: Co-developing VedaWeb, a Digital
Research Platform for Old Indo-Aryan Texts
Börge Kiss (Institute for Digital Humanities)1, ✉ Daniel Kölligan (Dept. of
Linguistics – Historical-Comparative Linguistics)1[0000-0002-3134-8398], Francisco Mondaca
(Cologne Center for eHumanities)1, Claes Neuefeind (Institute for Digital Humanities,
Data Center for the Humanities)1, Uta Reinöhl (Dept. of Linguistics – General
Linguistics)1, 2[0000-0003-2829-682X] and Patrick Sahle (Cologne Center for
eHumanities)1[0000-0002-8648-2033]
1
University of Cologne, D-50923 Köln, Germany
2
Johannes Gutenberg University Mainz, D-55099 Mainz, Germany
b.kiss@uni-koeln.de
✉ d.koelligan@uni-koeln.de
f.mondaca@uni-koeln.de
c.neuefeind@uni-koeln.de
uta.reinoehl@uni-koeln.de
sahle@uni-koeln.de
Presenters: Daniel Kölligan (humanities scholar), Claes Neuefeind (digital expert)
1 Research Goal: the “Humanities Problem”
The traditional linguistic approach in dealing with large text corpora includes the
writing of concordances (or word indexes) to make co-occurrences of forms visible
and to enable researchers to detect the frequency of usage patterns. The determination
of meanings, functions, syntactic patterns etc. are mostly based on the individual
assessment of a specialist drawing their conclusions from their intuition based on
personal reading experience and more or less implicit knowledge of the texts (cf. e.g.
[12, 20]). However, the more the quantity and quality of data increase, the more
intractable these resources become for such traditional “tools” and approaches. At the
same time, the recent wave of digitizing textual data makes the application of
computational methods unavoidable in order to ensure the highest possible scientific
standard in quantitative terms. The more information resources are made accessible,
systematically prepared and annotated, the more urgent the need for their formal
analysis becomes. The project described below is the interdisciplinary endeavour to
prepare such a resource for the linguistic analysis of a large corpus of richly annotated
textual data.
The objective of the VedaWeb project is to develop a web-based research platform
for linguistic and philological work on Old Indo-Aryan texts. To this aim, multiple
textual, lexical and other types of linguistic data are integrated in a single digital
research environment. Various layers of texts and annotations are made searchable
36
and are linked with lexicographic information. At a later stage, the platform will offer
personalized work spaces for researchers to enable collaborative work on the texts.
Long-term sustainability of data and software will be ensured through cooperation
with the Data Center for the Humanities at the University of Cologne
(http://dch.phil-fak.uni-koeln.de). In order to build VedaWeb, a group of historical
linguists, general linguists and computational linguists have teamed up with
specialists in digital humanities at the University of Cologne. In addition, the project
team collaborates with several international co-operation partners (e.g., from the
University of Zurich and from Harvard University).
For illustration, concrete examples of humanities research questions that will be
possible to address working with the VedaWeb platform include the following: In
contrast to the rather intuitive description of meanings as described above, linking
lexical units with and measuring their distance to all other units will allow to form
networks of co-occurrences, enabling a more systematic and better-controlled
semantic analysis. Linear distances on the one hand and co-occurrence frequencies on
the other serve as variables for the configuration of these networks. This possibility
will also further the study of lexical fields that so far had to be carried out “by hand”
(cf. [9, 10, 17]). In the realm of morphology, given the data stored in VedaWeb, it
becomes possible to study allomorphic variation within Vedic Sanskrit, e.g. the
competing forms of the nominative plural masculine of stems in ending in -a- (e.g.
áśv-a- ʻhorseʼ), viz. -ās (áśvās ʻhorsesʼ) and -āsas (áśvāsas, also ‘horses’). With the
help of VedaWeb these are currently studied in a project at the University of Cologne
(http://ifl.phil-fak.uni-koeln.de/36486.html?&L=1), which tries to establish their
synchronic distribution in the Rigveda according to various criteria such as agency
and topic-hood and to propose hypotheses regarding their diachronic development.
2 Background and Starting Points
VedaWeb provides access to ancient Indo-Aryan texts written in Vedic Sanskrit,
which are enriched with morphological and metric annotations as well as integrated
with lexicographic information, making them searchable according to
corpus-linguistic criteria. The pilot text of this project is the Rigveda, one of the oldest
and most important texts of the Indo-European language family and the oldest extant
one composed in Indo-Aryan, whose origin can be traced back to the late second
millennium BC. With an extent of c. 160.000 words in nearly 40.000 lines of verse
(comparable to the Homeric epics Iliad and Odyssey combined totalling c. 190.000
words), the Rigveda presents an extremely rich text corpus given its considerable
ancestry. At a later stage of the project, further texts such as the Atharvaveda (c.
170.000 words), Yajurveda and Vedic prose texts such as the Aitareya Brahmana (c.
100.000 words) and the Maitrayani Samhita (c. 120.000 words) are also to be
integrated into the VedaWeb platform to allow complex searches across several texts.
Further possible features include user-specific annotation tiers, semantic searches, and
the embedding of audio and video data of traditional recitals of the texts. The project
aims to advance research in all areas of Vedic linguistics and philology, for example
37
in syntax (e.g. referential null objects [11], non-configurationality [15]), morphology
(e.g. the Vedic vr̥kī-type [21], ya-presents [13]), metrics [18] and word formation
(e.g. compounds [19]).
The starting point of the project is a complete morphological annotation of the
Rigveda, which was carried out at the University of Zurich over several years, where
each word form has been annotated according to morphological categories (nouns:
case, number, gender, verbs: tense, mood, number, person, voice, etc.). In addition,
metrical information (provided by Kevin Ryan, Harvard University,
http://www.meluhha.com/rv/) and syntactic information from different completed and
ongoing research projects will be made available in VedaWeb. Metrical data for each
word form note the frequency of attestation, the number of syllables, the metrical
weight template (heavy and light syllables), any metrical variants (e.g. indra vs.
indara) and the number of occurrences in different positions in the verse. Syntactic
information comes from two sources: H. Hettrich (University of Würzburg) and O.
Hellwig (Heinrich Heine University Düsseldorf) [8] have annotated all verbs and their
arguments (e.g., direct objects) of the Rigveda as part of Hettrich’s studies on the
syntax of case in Vedic. This will allow searches for semantic categories and their
morphological surface expressions (e.g. “Which morphological cases are used to
denote instruments?”). Based on the Haig/Schnell annotation scheme GRAID
(Grammatical Relations and Animacy in Discourse [7]), a project team lead by PI
Reinöhl (http://sfb1252.uni-koeln.de/b03.html) is currently annotating selected Vedic
texts for syntactic and pragmatic information which will allow searches according to
types of referents (+/- human, +/- animate), clause types (e.g. main vs. relative
clause), etc.
In the course of the project, multiple research and analysis tools are integrated into
the platform. These include a combined search function for linguistic parameters
(including lemma, word form, morphological and metric information), links for each
word to entries in the standard dictionary of the Rigveda by Hermann Grassmann [3],
the display of translations (including [2, 4, 6, 16]) as well as commentaries [14, 16],
and the possibility of exporting sections of annotated text according to criteria
selected by the user.
A key feature is to link the Rigveda with the C-SALT (Cologne South Asian
Languages and Texts, http://c-salt.uni-koeln.de/) application programming interfaces
(APIs) for Sanskrit Dictionaries (https://api.c-salt.uni-koeln.de/). Based on a TEI
(Text Encoding Initiative) data model, the word forms in the text are linked to the
respective lemmas in Grassmann’s dictionary. In this way, it will also be possible to
create links to lemmas in further Sanskrit dictionaries, for example to enable
comparative, cross-dictionary searches (such as the DFG-funded
Nachtragswörterbuch des Sanskrit at the University of Halle with whom a
collaboration to this aim is planned, http://nws.uzi.uni-halle.de/). The direct linking of
text and dictionary is a unique feature of the VedaWeb platform compared to existing
resources of Old Indo-Aryan texts, e.g. the Thesaurus Indogermanischer Text- und
Sprachmaterialien (TITUS, http://titus.uni-frankfurt.de).
38
3 Realization: the “Technical Solution”
The VedaWeb platform is implemented as a web application based on the Spring
framework (https://spring.io). Spring serves as middleware providing controllers for
data handling, service orchestration and different views on the data. Since the
VedaWeb search should allow for linguistically motivated queries on different levels
of annotations by means of complex, combined search criteria, a tailored search logic
was implemented on the basis of the established search server Elasticsearch
(https://www.elastic.co/). This solution provides fast search functionality and
enrichment of search results (e.g. highlighting of lemmata vs. word forms). The
frontend is implemented as a single-page application (SPA) based on React
(https://reactjs.org) for building interactive front-end components. It makes use of Ant
Design (https://ant.design/) for a clear and effective interface.
The TEI model adopted for VedaWeb plays an important role both with regard to
the technical implementation as well as for data sustainability. As a cooperative
project, the various sets of information to be integrated into VedaWeb have different
sources and formats. For this reason, the various data resources had to be standardized
in order to be employed in the VedaWeb application and made available for external
projects. TEI provides a well-documented model and the necessary flexibility to
achieve this goal, especially with regard to long-term sustainability.
The Rigveda has been modelled mainly according to an inline-markup paradigm:
all of its text versions, annotations and translations are available in one TEI document.
Versions are aligned on the level of the stanza while linguistic annotations are added
to the single word forms of the chosen ‘Leittext’ via feature structures [22]. The
exception to this markup-style are the references to dictionaries, which are encoded as
stand-off markup (https://wiki.tei-c.org/index.php/Stand-off_markup). In the TEI
document, we only annotate the reference to a lemma in Grassmann’s dictionary,
which is also available in TEI format through an API. The motivation for modeling all
data in one document is, on the one hand, to show all the data per stanza in one single
source and, on the other hand, to simplify the process of exporting the data in TEI
format. To enable data export for users is a key feature that is planned to be
implemented in VedaWeb in the next project phase. In the web application, TEI data
is imported and stored in a document-oriented database (mongoDB, see
https://www.mongodb.com/), largely reflecting the original structure of the TEI
model. This allows for fast data delivery and flexible data manipulation. Additionally,
the use of a database is a prerequisite for implementing personalized work spaces for
researchers to add, edit and share data, which requires at least a combination of
versioning functionality (revision history) and user management.
To ensure the sustainability of the data integrated and software created within the
project, VedaWeb collaborates with the Data Center for the Humanities (DCH), which
is a “CLARIN Knowledge Centre for linguistic diversity and language
documentation” (see http://ckld.uni-koeln.de/). Furthermore, the DCH is a CLARIN
C-Centre (https://centres.clarin.eu/centre/47) and has applied for B-Centre status
39
(currently in the certification process). As such, the DCH hosts the data of the
VedaWeb project, ensures their long-term archiving and provision, and will also take
care of the data and software produced within the project beyond the end of the
funding period.
4 Background and Starting Points
Before we address more specifically the challenges and opportunities arising in this
collaboration, we sketch the general structures and dynamics of collaboration within
the VedaWeb team, which play a key role for the success of the project.
The project team has been on a steep learning curve with regard to reaching a
shared language and understanding of the tasks and challenges arising in the project
for the different team members. This is not only true of the chasm between the
“linguistic” and “technical” camp respectively, but applies on a more fine-grained
level. The following diagram illustrates the fields of competence (narrow bars) as well
as the de facto work areas (thick bars) of the various team members and primary
collaboration partners. The diagram makes clear that the team members do not
actually fall into two neatly separated domains, but that the range of competence areas
are carved up in different ways with overlapping responsibilities.
Although the project has a rather small staff basis financed by external funding
(DFG, German Research Foundation) with only one full researcher position (shared
among two DH specialists, BK and FM), two student assistant positions (NK and JH)
and a limited amount of money for the transition into a permanent hosting and
curation by the data center (JB), it is supported by a large team of researchers
involved either as principal investigators (UR, DK, PS, CN) or as collaborators in
Cologne (FR) or abroad (PW). Further colleagues such as K. Ryan (Harvard) and D.
Gunkel (Richmond) and consultants for special issues such as M. Gödel (Cologne) for
the TEI data model have also contributed to the project, but are not included in the
diagram for space constraints.
Fig. 1. Roles and coverages. Family constellation for the collaboration in the VedaWeb project.
40
As illustrated by the diagram, which we informally call our “family constellation”,
there is no simple bifurcation between digital humanities and linguistics. Rather, each
team member contributes to a certain range of domains, several ones of which are not
clearly or not purely either technical or linguistic. This differentiation and, crucially,
the overlap of competence areas among team members makes this project possible in
the first place, as no single team member could be competent in or responsible for the
whole range of domains.
In addition to differentiation and overlap, regular communication and close
feedback loops ensure productive, agile and goal-oriented work dynamics, the success
of which is mirrored by the fact that, half of the funding phase having elapsed, the
project is exactly in time with its planned schedule. Most online communication,
collaboration and data exchange takes place in GitLab (https://about.gitlab.com/),
where several of the linguistic bodies of data are stored, too. Using GitLab's issue
tracking system, team members are able to quickly provide feedback on each other’s
work and effectively react to changing requirements in the project.
Team meetings in person take place on a regular basis (roughly once a month on
average). In this way, developments can be steered into the right direction,
mis-communications can be detected early, and there is room for creativity and
adaptation. In fact, since the project is on time and running smoothly, the team is
already addressing certain steps that were originally believed to be beyond the first
funding period, such as the inclusion of further text versions and translations. The
overlapping structure of the competence areas among the team members, paired with
the regular meetings, which are supplemented by highly regular (often daily) online
communication and collaboration, give rise to a sense of plasticity, i.e. to a dynamic,
adaptable development of the project. Given the outlined project dynamics, the
intermediate targets planned in the original application are reached so far and, at the
same time, the project development can be flexibly adapted and expanded to find
ideal solutions.
4.1 Simple and Challenging Issues
“What was easy and what was difficult – and why?” In general, team members at
the more technical and at the more linguistic pole were frequently surprised at what
exactly it was that was either easy or difficult for the others. To give some examples,
implementing multiple, combinable full-text searches was easily realisable, which to a
linguist who does not normally work with the support of elaborate digital search
functions was surprising. At the same time, the fact that it would be technically quite
difficult to have search functions run over diversely structured sets of data was not
expected. Conversely, from the point of view of the DH researchers, the significant
complexity of the Rigveda into different sub-structures including books, hymns and
verses of different length and partially different type turned out to be a surprisingly
complex and challenging data basis.
In the case of these and other examples, it was only possible to identify what was
easy and what was difficult, and act accordingly, through the outlined constant and
intensive communication among all project members. Furthermore, teasing out
41
exactly the level of complexity and time requirements for individual tasks was
enabled through the structure of differentiation and overlap with regard to competence
areas and responsibilities of the different team members.
4.2 Mutual Change of Views
“How did researcher and technician change each other’s way of looking at things?”
The linguistic researchers gained considerable new insights into the opportunities
afforded by a digital research platform endowed with powerful search mechanisms
and an integration of textual with lexicographic data. In developing the VedaWeb
platform, the linguists gained a much deeper understanding of the affordances of
building both a web-based application and a TEI model for the purpose of ensuring
data longevity. On the other hand, the technical researchers gained new insights into
the complexities of ancient texts and further linguistic resources, both with regard to
their respective internal structures as well as the linguistic needs of accessing those
resources. Numerous ones of these insights gained into each other’s fields are likely to
benefit the project members also above and beyond the VedaWeb project, as they are
representative of DH work and historical, corpus-linguistic work respectively.
4.3 Disciplinary Blind Spots
“Did they, for instance, make each other aware of blind spots they had?” From day
one of the VedaWeb project, the researchers and DH experts were in a constant
dialogue that frequently uncovered blind spots in various domains and on all sides.
Detecting these blind spots and addressing them is considered by the project team as
central to the hitherto significant advances in building the VedaWeb platform. In
particular, the repeated discussion of certain issues in much detail and in both
professional and laymen’s terms to include all team members in the best possible way
proved essential to reaching the goals of the first project phase.
For all team members, it proved essential to clarify the technical and data-oriented
terminology and the needs of both DH specialists and humanities scholars to establish
data models capable of reflecting the complexity of linguistic data. For instance, the
complexity and variety of the textual layers (including different versions of the
original text according to different orthographic conventions and sub-structures),
translations as well as levels of linguistic annotations (e.g. morpho-syntactic, lexical,
metric, lexicographic) was a major blind spot for the DH specialists. One challenge in
this regard is that the linguists often possess tacit knowledge of certain structural facts
and it takes an extended and detailed communication process to make these explicit,
or to even realise the need for explicitness. In some ways, the linguists have only
gained a deepened understanding of the crucial need for explicitness for the purposes
of modelling in the course of the project. There have also been various further
domains in which blind spots became apparent, such as the need to address the
philological genesis of the texts or the need to clarify copyright and other legal issues.
In this regard, the linguists have been on a learning curve given the differences in
42
requirements of a web-based resource in comparison to their normal academic outlets
(i.e. publication of books or journal papers).
4.4 The Whole Is More than the Sum of Its Parts
“Did the combination of thinking from a DH research question and thinking from a
technical solution lead to new insights?” The integration of textual and lexicographic
data, as well as various types of linguistic annotation layers into a digitized and
standardized format modelled in TEI revealed manifold inconsistencies and gaps in
the input. In order to address these challenges, a detailed correction of, e.g., mappings
between textual data and lexical entries has been ongoing since the beginning of the
VedaWeb project. In this way, the transfer to digital formats has led to a significant
and necessary increase in quality of the linguistic data.
Besides the new possibilities with regard to, e.g., combined searches across
multiple layers of text and annotations, the digitization of the Rigveda also opens up
fields that have hitherto been hardly accessible to specialists of Old Indo-Aryan texts.
This is true, for instance, of semantic searches that will enable a novel type of
computerized research by linguists, philologists, philosophers, and other specialists of
Old Indo-Aryan texts.
4.5 Towards Improved Collaboration
“How could better training or education of scholars and digital experts make
collaboration easier, more effective and more efficient?” DH researchers collaborate
with researchers from manifold disciplines, so that it seems unrealistic to include in
their training specifics of any one humanities discipline. However, DH specialists
need a general understanding of the objects of study in the humanities, of research
questions and research methods, knowledge of which should certainly be included in
their training. As the digital experts are all graduated in Digital Humanities (instead
of, e.g., computer science), the project team members of VedaWeb have such training
and in part previous experience with specifically linguistic projects, and were
accordingly ideally prepared. Any specific insights that go beyond this general
background was then gained in the course of the project through the outlined
processes of regular and detailed communication and collaboration.
Similarly, detailed training in DH methods, approaches and resources may not be
possible or necessary to include in a linguistics training. However, a general
understanding of the different fields of DH work is certainly highly desirable,
especially given the intense, ongoing digitization of research in the humanities
disciplines. For instance, the fact that there is a significant difference between
building a web application, on the one hand, in contrast to ensuring sustainability of
data by modelling them in TEI, on the other hand, is an insight into the nature of DH
projects that is likely to benefit collaboration from early project stages. The
insufficient insights of the humanities researchers into these components of technical
DH work was a hindering factor in the early stages of VedaWeb and a training for
humanities researchers that includes a general understanding of these and other
43
general structures and processes is likely to be beneficial for collaborative work
across disciplines. In particular, this would involve developing a deepened
understanding for the need of modelling and formalizing knowledge structures,
making them explicit and thus usable for computational implementation. Moreover,
there is the need for a basic understanding of the interaction of different technical
components including data formats, databases, search engines, program logic, web
interfaces and usability.
5 Conclusion
We have described an instance of the “humanities problem” for linguists of handling
large textual corpora, in this case the corpus of Old Indo-Aryan texts. In this project,
the “technical solution” consists of developing a formal data model for the given
knowledge domain and the construction of a web-based research platform that
integrates multiple layers of textual data (different versions of the original text and
various translations) with linguistic annotations (e.g. morpho-syntactic and metrical
annotations) as well as with lexicographic data via APIs to specialized dictionaries.
Half of the funding time having elapsed, it has become clear that it is not only helpful
to collaborate closely among the different team members, but that a continuous and
intense process of communication, exchange and replanning is crucial for the
realization of the project. In particular, regular discussion to clarify terminology, on
the one hand, and the uncovering of blind spots among team members, on the other
hand, have proved indispensable. By engaging in such close collaboration, the project
has been able to stay in time with its planned schedule despite a long initial phase of
finding a common understanding and language, which is in part ongoing. Above and
beyond the communicative practices adopted, the project benefits significantly from a
diverse team with a variety of backgrounds and competences. A further advantage is
that the DH specialists in the team are already experienced in handling linguistic data.
However, we believe that interdisciplinary project teams in general can reach a
comparable level of mutual understanding when embracing particularly close
collaboration [1, 5]. This involves the continuous discussion and clarification of
terminology, of data formats and of models, as well as short feedback loops to create
a shared understanding of humanities research questions and to reach tailored DH
solutions.
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