Opening up cultural content in non-standard language
data through cross-disciplinary collaboration: insights on
methods, process and learnings on the example of
Amelie Dorn1[0000-0002-0848-8149], Yalemisew Abgaz2[0000-0002-3887-5342] and Eveline Wandl-
1 Austrian Centre for Digital Humanities, Austrian Academy of Sciences,Vienna, Austria
2 ADAPT Centre, Dublin City University, Dublin, Ireland
email@example.com; firstname.lastname@example.org; eveline
Abstract. Understanding collaboration between researchers of different disci-
plines requires an ability to embrace multiple views and perspectives, and com-
municative efforts. This paper thus provides insights on methods, processes and
results of a cooperation in Humanities research supported by semantic technolo-
gies with the aim of accessing and opening up cultural knowledge contained in a
non-standard language resource. The collaborative undertaking is carried out
within a Digital Humanities project and an Open Innovation framework. Meta-
disciplinary learnings offer insights on factors fostering mutual understanding,
knowledge translation and mutual benefits.
Keywords: Cross-disciplinary cooperation, cross-organizational collaboration,
1 Introduction & Background
Culture is a complex phenomenon that offers grounds for analysis in academia, society,
arts, etc. from various perspectives . It encompasses several aspects of a society and
has been widely expressed and conveyed over the centuries by words, stories, songs,
poems, paintings, writings and several other methods most typically through the me-
dium of language. Culture and language are thus tightly interwoven concepts that trans-
cend several societies in time. In recent times, there has thus been a trend in the Hu-
manities in preserving cultural content, mostly contained in written texts, taking lan-
guage as a first access point. With the support of modern technological tools and the
ever growing capacities of digital methods and devices, also otherwise hidden or im-
plicit cultural knowledge contained in Humanities data can be made visible and acces-
sible. Language data thus needs to be available digitally and in technologically en-
hanced and systematic formats to be accessed and used by the wider community of the
modern era, for it to be ultimately preserved through re-use and connectability.
In this paper, we address the collaboration between Humanities scholars and seman-
tic technologists in a Digital Humanities context (the exploreAT! project)  on the
example of a historic language resource (DBÖ) [3, 4]. We discuss the opening up and
exposition of this traditional non-standard German language collection using semantic
modelling which exploits existing semantic web standards to represent and facilitate a
common representation and interpretation of these cultural resources. Ontologies from
different domains and developed by our team are integrated and used to represent the
traditional resources to enhance their discoverability and usability in both independent
manner and integrated with other similar standardized resource.
We here report on our collaboration results, the humanities background to our re-
search question, the technical methods and implementation, but also on another im-
portant yet often unmentioned aspect of language in such cross-disciplinary collabora-
tions, namely the translation of knowledge and expertise across disciplines. Openness
to learnings, mutual understanding and communication are key elements in a founda-
tion of bringing about successful results
2 Opening up cultural contents of a traditional language
resource: the exploreAT! project
exploreAT! is a current DH project which aims to unveil cultural information contained
in a non-standard language resource (DBÖ) [Database of Bavarian dialects in Austria;
] by drawing on and combining digital methods and tools from different disciplines
(semantic technologies, visualisation prototyping, crowd science) (cf. ). At the heart
of the project lies the fundamental research question originating from the Humanities
background, which asks how to enable access to a non-standard language resource
through a cultural lens, giving insights on the conceptualisation of the world and the
local society at the time. In this context, the DBÖ resource offers a wealth of not only
valuable language data, but also rich cultural content. The database counts around a
total of 3.5 million entries, including original data collection questionnaires, answers
as well as other digitized excerpts of folklore literature. Originally collected in the area
of the former Austro-Hungarian empire with the aim of capturing the speech of the
local population, the former collection and following digital preparation was already a
huge collaborative effort across persons of various professions, backgrounds and func-
tions, offering detailed documented cultural and societal insights on topics of everyday
life (e.g., festivities, professions, nature, food, etc). In particular, our current efforts
concentrate around the topic of food, which offers rich grounds for analyses, connect-
ability as well as scientific and societal relevance. Through the support and application
of semantic tools, this implicit cultural knowledge can be accessed and connected to
other sources and resources for multilingual and multicultural comparison.
3 Cross-cultural Team Communication and Knowledge
Exchange: Methods & Tools
The exploreAT! project is all the more interesting as it not only combines cross-disci-
plinary expertise, but also collaborators of very different cultural and linguistic back-
grounds, located across Austria, Spain and Ireland. Methods and tools used for team
communication and knowledge exchange are thus key in harmonising and leveraging
results and communicating tasks, but also addressing challenges or uncertainties in the
workflow. In the wider context of exploreAT!, a combination of digital and analogue
methods and tools are employed for ideation (e.g. agile and design thinking tool kits),
communication across team members (e.g. web-based project management and com-
munication technologies) or for capturing project ideas and development.
In this paper we concentrate on the description of the specific collaboration scenario
which focuses on the creation of the semantic data model. This collaboration arises out
of the humanities research question on how to make cultural knowledge in a language
resource accessible, discoverable and connectable. In this particular context, current
digital tools for communication and task management (Slack, Trello, Skype) were em-
ployed, as well as regular face-to-face meetings. While online tools were used for fre-
quent exchange, face-to-face meetings served more specifically for discussions on ma-
jor project goals, creating work plans or for joint team meetings including also project
members. In order to implement collaborative writing, editing or brainstorming a free
web-based software office suite was used that could be accessed from any computer
with an internet connection.
Drawing on these tools, in what follows we elaborate on the methods, collaborative
processes and learnings on the example of the composition of the semantic model [6,7]
based on the Humanities research question and resource.
4 Cross-disciplinary Collaboration: the example of creating a
Cultural Semantic Data Model
4.1 First processes towards joint collaboration for Semantic Modelling
The aim of the semantic modelling in the context of exploreAT! was to enable the dis-
covery of cultural content in our language collection and connect it to other multilingual
and multicultural resources using LOD . The data collection questionnaires and re-
lated questions served as the initial access point to the remainder of the collection and
to enable connectability to other resources. The modelling further served to understand
the semantics of the core entities as defined by the humanists and as contained in the
collection, and to represent them and their relationships using existing up-to-date se-
mantic web technologies and standards. With the language collection being focused on
a specific domain (non-standard language), and the overall method used to collect the
data dating back to the beginning of the 20th century, it was crucial for the semantic
technologists to collaborate in direct exchange with the humanists. In our case, the col-
laboration involved three major teams. The first team (humanists) consisted of the do-
main knowledge experts who were involved in or had in-depth knowledge about all
steps of the original data collection, organisation and utilisation. The second team (lin-
guists, lexicographers) are researchers in the area of socio-cultural linguistics and re-
lated fields, and the third team (technical experts) comprised ontology engineers and
semantic web experts responsible for developing the semantic model and uplifting the
collection using a linked open data (LOD) platform. The collaboration example we re-
port on here, evolved in three steps.
1. The first joint work laid the foundation for understanding the overall area of
expertise and the fundamentals of the dataset.
2. The next step involved collaborating for modelling the core entities of the col-
lection using current semantic web technologies.
3. Finally, search, visualisation and exploitation of the results is presented.
Each of the three steps is described in the following sections.
4.2 Methods and interactions enabling access to implicit data knowledge
Understanding and identifying the implicit knowledge contained in the language col-
lection in general and the detailed meaning and interpretation of the cultural and lin-
guistic entities, in particular, was among the challenges largely faced by both technical
experts and linguists. As soon as the semantic modelling process started, the gap be-
came visible in that much of the knowledge which is useful to understand the collection
is not self-contained in the data. Thus, it became necessary to gain a deeper knowledge
of the data from sources other than the collection itself. Especially for the technical
experts, this became a challenge as their objective was to semantically organise and
describe the content. Initially, all available information was shared among the teams on
the cloud platforms used in the project. This included several resources such as publi-
cations describing the collection, notes and change logs. Although the information
helped the technical experts to better understand the collection, it generated new ques-
tions to the humanists, given the complex structuring of the materials, resulting in less
productive weekly meetings and only partially satisfactory advancement. The process
became time-consuming as technical experts were remotely located from the human-
ists, and knowledge experts could not provide the necessary information at the same
pace as the technological advancement proceeded.
As communication by digital means only didn’t prove optimal, resorting to a different
form of knowledge exchange, namely face-to-face meetings, became inevitable. The
first face-to-face meeting on the topic of semantic modelling brought the different
members involved (humanists, domain experts and technical experts) together in a
workshop setting with the aim of building a common understanding of the collection,
the methods, resources and techniques used for the original data collection process and
to investigate other possible sources of information. This collaboration workshop took
place at exploration space @ ACDH-OeAW in Vienna. The workshop provided valu-
able insights for both humanists and technical experts as it initiated discussions on top-
ics, such as the identification of cultural content indicators, identification of relevant
data fields, or task distribution and enabled the humanists to create new structures for
cultural content discovery supporting and enhancing the semantic modelling process.
The workshop paved the way for opportunities on planning and proposing a concrete
way forward in terms of tasks and workflows, and gave team members a solid under-
standing of the challenges and complexities involved, and made its contribution to elicit
the requirements of each team. Since the initial meeting, a number of similar workshops
were conducted in Dublin, Salamanca, Vienna and CERN by incorporating different
stakeholders to discuss new opportunities.
Oftentimes a unilateral attempt to model a non-standard language resource can result
in an ill-representation, potentially leading to less usability. This face-to-face interac-
tion enabled the discovery of key aspects which would have been challenging, time-
consuming or even more complex to communicated by digital or written means only.
The semantic modelling exercise resulted in the identification of cultural and linguistic
indicators from the side of the humanists and a conceptual model of the collection and
its representation using an ontology in owl language, from the technical experts. The
resulting ontology and its representation is discussed in detail in [7,9].
A key takeaway for collaboration, is that face-to-face meetings and direct exchange
may foster team spirit among collaborators, potentially fuelling further collaboration
beyond the current project. In addition, it allows for cross disciplinary collaboration of
seemingly far apart areas and benefits members in terms of understanding potential
complexities involved in other areas of expertise.
4.3 Synthesizing Humanities and technical expertise towards a first prototype
A next step in the joint collaboration included establishing individual workflows for
each team and working towards first common results, a cultural data model for non-
standard data questionnaires . Through weekly exchanges and updates using digital
communication channels, advancements from both humanists and technical experts
were consolidated. Particularly in the joint creation of a data model, the consolidation
of views from a semantic web expert and a Digital Humanities are key, as naming con-
ventions or details of representations may vary significantly. Bringing these differences
together and narrowing the gap on the representation is crucial, often triggering further
revisions, where trade-offs need to be made.
Finally, a first prototype of the data model was presented and discussed with other
members of the exploreAT! project in a second workshop. There opportunities arose
for the technical expert to engage other project members in a constructive discussion
by demonstrating the solution and the application areas. This further face-to-face meet-
ing enabled the technical expert to perform several refinements of the model, including
in cleaning noisy data, and it also paved the way for further discussion of the architec-
ture of the implementation. As a result of the direct interaction, several key decisions
could be taken and implemented by all experts involved. Any follow-up communica-
tion could thus be continued in online meetings and standups via Skype and Slack chan-
nels in regular intervals.
4.4 Creating exploration paths for mutual understanding: facilitating search,
visualisation and exploitation of the results
After collaborating in smaller groups for the purpose of elaborating the data model, the
next step involved the consolidation and communication of results to the other project
members and areas of expertise, such as visual prototyping.
Translating the queries provided by the humanists into a high level technical query lan-
guage proved challenging. The purpose of the semantic modelling and annotation of
the collection was to enable the users to discover cultural content in a non-standard
language collection and explore their semantic relationships discovering new insights
and support for their research hypotheses. However, providing the resulting semantic
research collection with a query user interface often fails in serving the purpose. To
address this gap, the initial queries of the humanists were translated to exploration paths
in order to elicit the exact requirements. This process involved navigating through the
data collection step-by-step, building navigation paths of one or two steps at a time to
include further requirements after identifying an initial pivotal query. The exploration
paths laid a foundation for the semantic web and visualisation experts to understand the
requirements of the users in their own perspectives and to interpret the queries of the
target users. At the same time, it enabled the humanists to understand how the semantic
data could be efficiently exploited to support their research questions. This was a sig-
nificant step in the collaboration to understand how the semantic modelling process
enhanced the requirements of the users and to provide additional customisable user in-
terfaces to enable the users to pose their own questions.
5 Insights & Conclusion: metadisciplinary learnings
Our collaboration of Humanities research supported by semantic technologies has
brought about valuable insights and learnings regarding the knowledge exchange pro-
cess in terms of creating scientific results, but also in terms of team composition that
can prove helpful for training purposes. From our experience, we can report that em-
bracing team diversity brings wealth in both expertise and perspectives. Bringing to-
gether researchers of various roles enables a more complete picture and analysis of
various perspectives in terms of addressing a particular research question, ultimately
consolidating results. What is a key prerequisite, however, is the individual ability to
bringing openness and flexibility to a team, which, if lacking, may pose difficulties to
the collaboration process. In addition, fostering mutual understanding for involved dis-
ciplines can be assured by taking part in training courses in order to obtain basic
knowledge in, for example semantic technologies, which also proved beneficial in
terms of communication and translation of knowledge. Finally, experimenting and re-
flecting on novel methods of communication and idea-finding may additionally con-
tribute to bringing together different perspectives and enable better mutual understand-
ing. The team applies and analyzes novel approaches towards collaboration in an Open
Innovation  framework, for example working together with designers  to in-
crease the learning curve and potential mutual benefits.
Based on the learnings from exploreAT!, a virtual and physical space for experimen-
tation and innovation has been funded, namely exploration space, currently a best prac-
tice example of the Open Innovation platform of the Austrian government (http://open-
This research is funded by the Nationalstiftung of the Austrian Academy of Sciences
under the funding scheme: Digitales kulturelles Erbe, No. DH2014/22 as part of the
exploreAT! project, carried out in a collaboration with the Adapt Centre, DCU.
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