Understanding collaboration between researchers of different disciplines requires an ability to embrace multiple views and perspectives, and communicative efforts. This paper thus provides insights on methods, processes and results of a cooperation in Humanities research supported by semantic technologies 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. Metadisciplinary learnings offer insights on factors fostering mutual understanding, knowledge translation and mutual benefits.
In this paper, we address the collaboration between Humanities scholars and
semantic technologists in a Digital Humanities context (the exploreAT! project) [
We here report on our collaboration results, the humanities background to our research question, the technical methods and implementation, but also on another important yet often unmentioned aspect of language in such cross-disciplinary collaborations, namely the translation of knowledge and expertise across disciplines. Openness to learnings, mutual understanding and communication are key elements in a foundation 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;
[
The exploreAT! project is all the more interesting as it not only combines cross-disciplinary expertise, but also collaborators of very different cultural and linguistic backgrounds, 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 communication 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 employed, as well as regular face-to-face meetings. While online tools were used for frequent exchange, face-to-face meetings served more specifically for discussions on major 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 [
4.1
The aim of the semantic modelling in the context of exploreAT! was to enable the
discovery of cultural content in our language collection and connect it to other multilingual
and multicultural resources using LOD [
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 collection 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
Understanding and identifying the implicit knowledge contained in the language collection in general and the detailed meaning and interpretation of the cultural and linguistic 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 became 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 publications describing the collection, notes and change logs. Although the information helped the technical experts to better understand the collection, it generated new questions 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 humanists, 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 valuable insights for both humanists and technical experts as it initiated discussions on topics, 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 understanding 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
interaction enabled the discovery of key aspects which would have been challenging,
timeconsuming 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 [
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
nonstandard data questionnaires [
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 meeting 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 architecture 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 communication could thus be continued in online meetings and standups via Skype and Slack channels in regular intervals. 4.4
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 language 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 significant step in the collaboration to understand how the semantic modelling process enhanced the requirements of the users and to provide additional customisable user interfaces to enable the users to pose their own questions. 5
Our collaboration of Humanities research supported by semantic technologies has
brought about valuable insights and learnings regarding the knowledge exchange
process 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
embracing team diversity brings wealth in both expertise and perspectives. Bringing
together 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
disciplines 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
reflecting on novel methods of communication and idea-finding may additionally
contribute to bringing together different perspectives and enable better mutual
understanding. The team applies and analyzes novel approaches towards collaboration in an Open
Innovation [
Based on the learnings from exploreAT!, a virtual and physical space for experimentation and innovation has been funded, namely exploration space, currently a best practice example of the Open Innovation platform of the Austrian government (http://openinnovation.gv.at/portfolio/oeaw-exploration-space/).
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.