The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To this end, this poster introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the NFDI4DS consortium. Built upon the NFDICore ontology and mapped to the Basic Formal Ontology (BFO), this ontology serves as the foundation for the NFDI4DS knowledge graph currently under development.
CEUR ceur-ws.org
Netherlands (H. Sack)
https://tinyurl.com/3cx37b9x (G. A. Gesese); https://shorturl.at/UwDND (J. Waitelonis);
resources, including individuals, organizations, projects, data portals, and more. NFDICore
provides mappings to a broad range of standards across diferent domains, such as the Basic Formal
Ontology (BFO) [
NFDI4DataScience (NFDI4DS)5 is one of the NFDI consortia and its project aims to enhance the accessibility and interoperability of research data in the domain of Data Science (DS) and Artificial Intelligence (AI). Data Science (DS) is a multidisciplinary field combining diferent aspects of mathematics, statistics, computer science, and domain-specific knowledge to extract meaningful insights from diverse data sources. DS and Artificial Intelligence (AI) involve various artifacts, e.g., datasets, models, ontologies, code repositories, execution platforms, repositories, etc. The project achieves this by linking digital artifacts and ensuring their FAIR (Findable, Accessible, Interoperable, and Reusable) accessibility, thereby fostering collaboration across various DS and AI platforms. To this end, the NFDI4DS Ontology (NFDI4DSO) is built.
As mentioned earlier, NFDI4DSO is created in a modular fashion, building upon NFDICore.
Similar to NFDICore, the NFDI4DSO ontology is developed using a bottom-up, iterative,
usercentered approach. NFDICore comprises 51 classes, 55 object properties, 8 data properties,
18 annotation properties, and 5 SWRL rules [
NFDI4DSO contains various kinds of classes such as processes, roles, and independent continuants. For instance, Figure 1 depicts how NFDI4DSO represents the relationship between the independent continuant nfdi4dso:SonjaSchimmler and her spokesperson role nfdi4dso:SpokespersonRole by mapping it to BFO. By using roles and processes, NFDI4DSO enables a detailed representation of the relationship between diferent entities enhancing the ontology’s level of expressivity. On the other hand, to support easier integration and use of less complex relations, shortcuts are also introduced to simplify the ontology by implementing easy-to-use
4https://git.rwth-aachen.de/nfdi-matwerk/ta-oms/mwo 5https://www.nfdi4datascience.de/ 6https://ise-fizkarlsruhe.github.io/nfdicore/ 7http://www.scholarlydata.org/ontology/doc/#toc direct shortcut properties, which can be expanded to fully-fledged BFO-compliant complex path expressions. For instance, in Figure 1, the shortcut relation nfdi4dso:spokesperson is provided and its corresponding SWRL8 rule is given below.
Person(?p) ∧ Consortium(?c) ∧ SpokespersonRole(?sr) ∧ Leading(?l) ∧ participates in(?p, ?l) ∧ participates in(?c, ?l) ∧ has role(?p, ?sr) ∧ realised in(?sr, ?l) → spokesperson(?c, ?p) Ontology Implementation The Protégé ontology editor 9 for the OWL-based formalization of terminological knowledge has been used to develop and implement NFDI4DSO. Widoco10 has been used to create an enriched and customized documentation of the ontology automatically. The stable version of the ontology NFDI4DSO v1.0.0 is available at https://github.com/ISE-FIZKarlsruhe/NFDI4DS-Ontology/tree/main and the latest development version is at https://github.com/ISE-FIZKarlsruhe/NFDI4DS-Ontology/tree/develop-1.0.1.
The NFDI4DSO is designed to form the foundation of the NFDI4DS Knowledge Graph (NFDI4DSKG), which is currently under development. The NFDI4DS-KG consists of two main components: the Research Information Graph (RIG) and the Research Data Graph (RDG). RIG includes metadata about the NFDI4DS consortium’s resources, persons, and organizations, while the RDG encompasses content-related index data from the consortium’s heterogeneous data sources.
RIG serves as the backend for the NFDI4DS web portal, facilitating interactive access and management of this data. Both RIG and RDG will be accessible and searchable via the NFDI4DS Registry platform. Additionally, the NFDI4DS consortium plans to collaborate with other NFDI consortia to further integrate domain-specific knowledge into the RDG seamlessly. Currently, the first version of the NFDI4DS-KG 11 with RIG is publicly available. For example, to view the list of co-spokespersons of the NFDI4DS Consortium, you can either navigate through the data using SHMARQL12, as depicted in Figure 2 or query it using SPARQL, as shown in Figure 3.
This paper presents the NFDI4DS Ontology and its use for the NFDI4DS-KG that is currently under-development. The ontology facilitates the representation and interoperability of data science artifacts within and outside of NFDI4DS. NFDI4DSO is built on top of the NFDICore ontology and mapped to BFO and other ontologies. In the future, there is a plan to perform extensive ontology evaluation using competency questions based on the persona definitions from the NFDI4DS consortium.
This publication was written by the NFDI consortium NFDI4DataScience in the context of the work of the association German National Research Data Infrastructure (NFDI) e.V.. NFDI is financed by the Federal Republic of Germany and the 16 federal states and funded by the Federal Ministry of Education and Research (BMBF) – funding code M532701 / the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number NFDI4DataScience (460234259). 11https://nfdi.fiz-karlsruhe.de/4ds/sparql, https://nfdi.fiz-karlsruhe.de/4ds/shmarql 12https://shorturl.at/eNb5e