Evaluation of a Generic Approach for Designing Domain Ontologies Based on XML Schemas

The process designing domain ontologies from scratch is very time-consuming and is associated with a lot of effort. In the most cases, domain experts have defined XML Schemas, describing domain data models, before ontologies have been created. Our idea is to generate ontologies out of XML Schemas automatically using XSLT transformations in a first step, and to derive domain ontologies semi-automatically using SWRL rules in a second step. We apply our approach in order to reuse the information located in the XML Schemas for the design of domain ontologies. In this paper, we aim to verify the hypothesis, that the effort and the time delivering high quality domain ontologies using the developed semi-automatic approach is much less than creating domain ontologies in a completely manual way. We have applied the individual stages of the suggested approach to multiple different data models in the academic and the industry domain. In addition to that, we show one complete use case for which the traditional approach designing domain ontologies manually and the proposed approach have been applied – the DDI-RDF Discovery Vocabulary, which is an ontology of the social science metadata standard Data Documentation Initiative

A Hybrid Focus Group for the Evaluation of Digital Scholarly Editions of Literary Authors

Digital scholarly editions (DSEs) are becoming more and more important for the work of scholars in the humanities. Yet, little is known about how the end users benefit from DSEs in contrast to paper editions, which kinds of interfaces for digital editions are the most useful and how the user interface of digital editions can be improved systematically. In order to answer these questions, we collected qualitative and quantitative data through a user study with a hybrid focus group of humanities graduate students. Open task scenarios were designed to explore the usefulness of three DSEs. Our key result is that lack of usability can be a serious hurdle for users to effectively use the DSE. This leads the participants to prefer books over the DSE, although they do value the added benefits the DSE o ers in terms of additional content

Data-Seeking Behaviour in the Social Sciences

Purpose: Publishing research data for reuse has become good practice in recent years. However, not much is known on how researchers actually find said data. In this exploratory study, we observe the information-seeking behaviour of social scientists searching for research data to reveal impediments and identify opportunities for data search infrastructure. Methods: We asked 12 participants to search for research data and observed them in their natural environment. The sessions were recorded. Afterwards, we conducted semi-structured interviews to get a thorough understanding of their way of searching. From the recordings, we extracted the interaction behaviour of the participants and analysed the spoken words both during the search task and the interview by creating affinity diagrams. Results: We found that literature search is more closely intertwined with dataset search than previous literature suggests. Both the search itself and the relevance assessment are very complex, and many different strategies are employed, including the creatively "misuse" of existing tools, since no appropriate tools exist or are unknown to the participants. Conclusion: Many of the issues we found relate directly or indirectly to the application of the FAIR principles, but some, like a greater need for dataset search literacy, go beyond that. Both infrastructure and tools offered for dataset search could be tailored more tightly to the observed work processes, particularly by offering more interconnectivity between datasets, literature, and other relevant materials

NER on Ancient Greek with minimal annotation

This paper presents the results in the adaptation of a new workflow of Named Entity Recognition and classification applied to Ancient Greek. We used a model of data extraction and pattern discovery based on machine learning algorithms which is easily customizable for different languages. This allowed the creation of a dataset of automatically classified place-names and ethnonyms starting from a small manually annotated list. We worked on the assumption that premodern textual sources display a recognized systematicity in their linguistic encoding of space, which provides a test-case for automatic context-based methods. The idea is that we should be able to train the machine to recognize an entity from recurring elements in the context, without providing a large training dataset in advance

Enhancing FAIR Compliance in Research Data Infrastructures: Insights from Applications of the RDA FAIR Data Maturity Model and the F-UJI Automated FAIR Data Assessment Tool

We share experiences assessing KonsortSWD using two approaches (manual and automated assessments). We used the FAIR Data Maturity Model (RDA-FDMM), which proposes 41 FAIR indicators organized into three classes (essential, important, useful) and five assessment levels. We applied RDA-FDMM to KonsortSWD's PID service, aiming to assign PIDs to data elements below the study level (such as survey variables). The indicators were manually assessed using the pass-or-fail method. We used the F-UJI Tool to automatically assess the GESIS Search as a relevant repository in the context of KonsortSWD. Tools like F-UJI offer valuable feedback on how to improve FAIR scores by automated means. Our experience highlights the importance of evaluating both machine-readable and non-machine-readable elements. As the research ecosystem evolves, providing easily machine-readable metadata becomes increasingly important. We recommend adopting a "FAIR by design" approach early in product or service development to ensure FAIR principles are embedded in project outcomes.KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171

Enhancing FAIR Compliance in Research Data Infrastructures: Insights from Applications of the RDA FAIR Data Maturity Model and the F-UJI Automated FAIR Data Assessment Tool [Presentation]

We share experiences assessing KonsortSWD using two approaches (manual and automated assessments). We used the FAIR Data Maturity Model (RDA-FDMM), which proposes 41 FAIR indicators organized into three classes (essential, important, useful) and five assessment levels. We applied RDA-FDMM to KonsortSWD's PID service, aiming to assign PIDs to data elements below the study level (such as survey variables). The indicators were manually assessed using the pass-or-fail method. We used the F-UJI Tool to automatically assess the GESIS Search as a relevant repository in the context of KonsortSWD. Tools like F-UJI offer valuable feedback on how to improve FAIR scores by automated means. Our experience highlights the importance of evaluating both machine-readable and non-machine-readable elements. As the research ecosystem evolves, providing easily machine-readable metadata becomes increasingly important. We recommend adopting a "FAIR by design" approach early in product or service development to ensure FAIR principles are embedded in project outcomes.KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171