=Paper=
{{Paper
|id=Vol-3759/paper21
|storemode=property
|title=NFDI4DSO: Towards a BFO Compliant Ontology for Data Science
|pdfUrl=https://ceur-ws.org/Vol-3759/paper21.pdf
|volume=Vol-3759
|authors=Genet Asefa Gesese,Jörg Waitelonis,Zongxiong Chen,Sonja
Schimmler,Harald Sack
|dblpUrl=https://dblp.org/rec/conf/i-semantics/GeseseWCSS24
}}
==NFDI4DSO: Towards a BFO Compliant Ontology for Data Science==
https://ceur-ws.org/Vol-3759/paper21.pdf
NFDI4DSO: Towards a BFO Compliant Ontology for
Data Science
Genet Asefa Gesese1,2,∗ , Jörg Waitelonis1,2 , Zongxiong Chen3 , Sonja Schimmler3 and
Harald Sack1,2
1
FIZ Karlsruhe, Leibniz Institute for Information Infrastructure, Germany
2
Karlsruhe Institute of Technology, KIT, Germany
3
Fraunhofer FOKUS, Berlin, Germany
Abstract
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.
Keywords
Data Science, Artificial Intelligence, Ontology, Knowledge Graph, NFDI4DS
1. Introduction
The German National Research Data Infrastructure (NFDI)1 is a non-profit association founded
to coordinate the activities for establishing a national research data infrastructure. It comprises
26 consortia spanning a wide range of scientific disciplines, from cultural sciences, social sci-
ences, humanities and engineering to life sciences and natural sciences. The NFDI consortia
share common goals and concepts, such as their members, structure, data repositories, and
services [1]. To enhance interoperability across these consortia, the NFDICore ontology2 has
been developed. It acts as a mid-level ontology for representing metadata related to NFDI
SEMANTiCS 2024: 20th International Conference on Semantic Systems, September 17–19, 2024, Amsterdam, The
Netherlands
∗
Corresponding author.
Envelope-Open genet-asefa.gesese@fiz-kalrsruhe.de (G. A. Gesese); Joerg.Waitelonis@fiz-Karlsruhe.de (J. Waitelonis);
zongxiong.chen@fokus.fraunhofer.de (Z. Chen); sonja.schimmler@fokus.fraunhofer.de (S. Schimmler);
harald.sack@fiz-kalrsruhe.de (H. Sack)
GLOBE https://tinyurl.com/3cx37b9x (G. A. Gesese); https://shorturl.at/UwDND (J. Waitelonis);
https://www.fokus.fraunhofer.de/009785fd54551039 (S. Schimmler); https://www.aifb.kit.edu/web/Harald_Sack
(H. Sack)
Orcid 0000-0003-3807-7145 (G. A. Gesese); 0000-0001-7192-7143 (J. Waitelonis); 0000-0003-2452-0572 (Z. Chen);
0000-0002-8786-7250 (S. Schimmler); 0000-0001-7069-9804 (H. Sack)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
https://www.nfdi.de/
2
https://ise-fizkarlsruhe.github.io/nfdicore/2.0.0/
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
resources, including individuals, organizations, projects, data portals, and more. NFDICore pro-
vides mappings to a broad range of standards across different domains, such as the Basic Formal
Ontology (BFO) [2] and Schema.org [3] to advance knowledge representation, data exchange,
and collaboration across diverse domains. To address domain-specific research questions for
each consortium, NFDICore follows a modular architecture. Examples for modular extensions
include the NFDI4Culture ontology module CTO3 [4] and the NFDI-MatWerk ontology module
MWO4 , which are specifically designed for the cultural heritage and materials science domains,
respectively. In this paper, we present an ontology named NFDI4DSO for the data science
domain as a domain-specific modular extension of NFDICore.
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 different
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.
2. The NFDI4DataScience Ontology (NFDI4DSO)
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, user-
centered approach. NFDICore comprises 51 classes, 55 object properties, 8 data properties,
18 annotation properties, and 5 SWRL rules [5] (for details refer to NFDICore documentation6 ).
In NFDI4DSO, in addition to what is provided in NFDICore, 42 classes, 38 object properties,
9 data properties, and 8 SWRL rules are added. The NFDI4DSO ontology not only describes
various data science artifacts but also provides information about the resources of the NFDI4DS
Consortium, such as personas, consortium members, spokespersons, and task area leads. AS
in NFDICore, the classes introduced in NFDI4DSO are also mapped to the top-level ontology
BFO and also other ontologies such as schema.org, the FaBiO ontology [6], and the Conference
Ontology7 .
NFDI4DSO contains various kinds of classes such as processes, roles, and independent con-
tinuants. For instance, Figure 1 depicts how NFDI4DSO represents the relationship between the
independent continuant nfdi4dso:SonjaSchimmler and her spokesperson role nfdi4dso:Spokesper-
sonRole by mapping it to BFO. By using roles and processes, NFDI4DSO enables a detailed
representation of the relationship between different entities enhancing the ontology’s level
of expressivity. On the other hand, to support easier integration and use of less complex re-
lations, shortcuts are also introduced to simplify the ontology by implementing easy-to-use
3
https://gitlab.rlp.net/adwmainz/nfdi4culture/knowledge-graph/culture-ontology
4
https://git.rwth-aachen.de/nfdi-matwerk/ta-oms/mwo
5
https://www.nfdi4datascience.de/
6
https://ise-fizkarlsruhe.github.io/nfdicore/
7
http://www.scholarlydata.org/ontology/doc/#toc
Figure 1: Example of representing roles where the prefixes ro and obi represent http://purl.oboli-
brary.org/obo/ro.owl and http://purl.obolibrary.org/obo/obi.owl ontologies, respectively.
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 formal-
ization of terminological knowledge has been used to develop and implement NFDI4DSO.
Widoco10 has been used to create an enriched and customized documentation of the on-
tology 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.
3. NFDI4DSO in Use
The NFDI4DSO is designed to form the foundation of the NFDI4DS Knowledge Graph (NFDI4DS-
KG), 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.
8
https://ise-fizkarlsruhe.github.io/NFDI4DS-Ontology/#d4e7620
9
https://protege.stanford.edu/
10
https://github.com/dgarijo/Widoco
Figure 2: A screenshot of part of the SHMARQL interface with the list of NFDI4DS co-spokespersons
(refer to https://shorturl.at/eNb5e to navigate it fully.)
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-KG11 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.
4. Conclusion and Future Work
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.
Acknowledgments
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 NFDI4Data-
Science (460234259).
11
https://nfdi.fiz-karlsruhe.de/4ds/sparql, https://nfdi.fiz-karlsruhe.de/4ds/shmarql
12
https://shorturl.at/eNb5e
Figure 3: An example SPARQL query to provide a list of the co-spokespersons of the NFDI4DS Consor-
tium. (It possible to query it live at: https://nfdi.fiz-karlsruhe.de/4ds/sparql)
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