=Paper=
{{Paper
|id=Vol-3759/paper11
|storemode=property
|title=The Helmholtz Digitization Ontology: Representing Digital
Assets in the Helmholtz Digital Ecosystem
|pdfUrl=https://ceur-ws.org/Vol-3759/paper11.pdf
|volume=Vol-3759
|authors=Said Fathalla,Gerrit Günther,Leon Steinmeier,Christine
Lemster,Dorothee Kottmeier,Lakxmi Sivapatham,Pier Luigi
Buttigieg,Volker Hofmann,Stefan Sandfeld
|dblpUrl=https://dblp.org/rec/conf/i-semantics/FathallaGSLKSBH24
}}
==The Helmholtz Digitization Ontology: Representing Digital
Assets in the Helmholtz Digital Ecosystem==
https://ceur-ws.org/Vol-3759/paper11.pdf
The Helmholtz Digitization Ontology: Representing
Digital Assets in the Helmholtz Digital Ecosystem
Said Fathalla1,* , Gerrit Günther2 , Leon Steinmeier3 , Christine Lemster4 ,
Dorothee Kottmeier4,6 , Lakxmi Sivapatham5 , Pier Luigi Buttigieg4 , Volker Hofmann1,*
and Stefan Sandfeld1
1
Forschungszentrum Jülich GmbH, Institute for Advanced Simulation – Materials Data Science and Informatics (IAS-9),
Jülich, Germany
2
Helmoltz-Zentrum Berlin für Materialien und Energie, Berlin, Germany
3
Helmholtz Center Dresden Rossendorf, Dresden, Germany
4
GEOMAR Helmholtz-Zentrum für Ozeanforschung, Kiel, Germany
5
Deutsches Zentrum für Luft und Raumfahrt, Cologne, Germany
6
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Abstract
The Helmholtz Association is actively digitizing research outcomes to drive progress and innovation. The
vast volumes of digital data are diverse in terms of their formats and the semantic descriptions used in their
interchange, and storage. Therefore, a semantic frame of reference is required to facilitate interoperability
throughout the Helmholtz digital ecosystem. This paper presents the Helmholtz Digitization Ontology
(HDO), which is intended to serve that purpose. HDO is a mid-level ontology that contains concepts
representing digital assets relevant to the Helmholtz digital ecosystem, data creation, management, and
exchange. It is developed within the framework of the Helmholtz Metadata Collaboration (HMC) with
contributors from various scientific backgrounds. HDO serves as a harmonized semantic framework and
machine-actionable reference across all Helmholtz research fields.
Keywords
Metadata, Metadata Management, Data Management, FAIR, Harmonization, OWL, Bilingual ontology
1. Introduction
The Helmholtz Association comprises 18 research centers operating across Germany, focus-
ing on a wide range of scientific topics and methods within six research areas fields1 . The
Helmholtz Metadata Collaboration (HMC)2 is an association-wide operating platform that sup-
ports (meta)data harmonization and information engineering across all research fields intending
to make data within Helmholtz adhere to the FAIR principles [1] and establish an interoperable
FAIR data space.
Motivation and requirements. The heterogeneity of scientific contexts within Helmholtz
leads to ambiguity and conflicts regarding metadata semantics, e.g. in developed metadata
SEMANTiCS 2024: 20th International Conference on Semantic Systems, Sep 17–19, 2024, Amsterdam, The Netherlands
*
Corresponding author.
$ s.fathalla@fz-juelich.de (S. Fathalla); v.hofmann@fz-juelich.de (V. Hofmann)
© 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.helmholtz.de/en/research/research-fields/
2
https://www.helmholtz-metadaten.de/en
1
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Said Fathalla et al. CEUR Workshop Proceedings 1–5
schemas, tools or general communication between collaborators, when data is exchanged in
interdisciplinary collaboration. Due to their ability to establish clear context and relationships
between concepts [2], ontologies are widely used towards facilitating efficient and interoperable
data management and data exploitation. As such, ontologies are important towards making data
FAIR, specifically towards achieving interoperability and machine actionability. Thus, HMC
realized that it is required to provide a semantic frame of reference for all stakeholders involved
in Helmholtz’s digitization efforts. Contributors from all Helmholtz research fields have been
involved in this process.
Objectives. The main objectives of the Helmholtz Digitization Ontology (HDO) are: 1) Creat-
ing a standardized semantic framework with terminology that can reduce semantic uncertainty
and ambiguity and thereby increase semantic interoperability between various Helmholtz
systems. 2) Facilitating data integration in different Helmholtz systems, e.g., the institutional
Helmholtz Knowledge Graph [3] or domain-specific use cases. 3) Providing a basis for reasoning
based on existing data to allow inferring new knowledge, e.g., towards predictive analyses.
4) Supporting harmonized knowledge management within Helmholtz to facilitate decision-
making and the preservation of research findings.
2. Concepts Definitions
In HDO, we provide well-defined concepts with rigorous semantic and unambiguous definitions.
Class definitions aim to: 1) outline the intrinsic characteristics of the term being defined,
2) avoid circularity, 3) be neither excessively broad (to avoid ambiguity), nor too narrow (to
allow implementation of further sub-classes where necessary), and 4) be easily understood
using common, unambiguous terminology, which is important, especially concerning HDO’s
purpose of serving as a mid-level ontology that provides common understanding and reduces
miscommunication across different research fields.
To create class definitions, we follow Aristotelian logic, specifically, definitions adhere to
the genus-differentia form3 . Genus-differentia definitions follow the form: “A (the class
label) is a B (the genus or superclass) which is C (the differentia)”.
For instance, consider the definition of “JSON file”, which is “A file which conforms
to JSON format”. Here, the genus part is “file”, which is the superclass of JSON file, from
which all differentia and properties are inherited. The remaining part is the differentia, which
comprises the features distinguishing the currently defined term from its genus and siblings.
3. Development Strategy
The key aspects of HDO development are illustrated in Figure 1. The development is carried
out in three phases: initialization, implementation, and adoption and adaption.
1. Initialization phase: In the early stages of the development, an internal GitLab repository
was created to gather a set of core terms and their definitions in per-term YAML files. We
followed a template with keys such as definition, synonyms, comments, and seeAlso.
3
https://en.wikiversity.org/wiki/Dominant_group/Genus_differentia_definition
2
Said Fathalla et al. CEUR Workshop Proceedings 1–5
creator date modified date created contributor Class Axioms LODE
license
Metadata Logic
editor note Documentation ODK
BFO IAO OWL
RO Development PID
Reused
DC
ontologies full provenance
FOAF
acronym Bilingual
definition
broad synonym
Class German
label annotations
related synonym
English
comment
cross reference examples gloss plural exact synonym
Figure 1: Key development aspects used during HDO development.
2. Implementation phase: After collecting terms, YAML files were converted and
merged into one OWL file, where keys of the template were mapped onto existing and im-
ported annotation properties (e.g. definition was mapped to iao:definition). Fur-
ther development was carried out in the OWL file in a public repository4 , and managed
using the Ontology Development Kit [4]. HDO is made accessible via a persistent identi-
fier (https://purls.helmholtz-metadaten.de/hob/hdo.owl) and terms are derefrenced via their IRIs (e.g.,
HDO_00004001). PIDA is used to dereference a single ontology term IRI, and display the HTML
documentation5 on a web browser for human users or provide the OWL file for machines.
The class hierarchy is extended according to the following workflow: 1) Contributors create
a GitLab issue and propose a term definition and properties, 2) Collaborative discussion was
carried out within the issue thread. 3) Upon general agreement, the class was implemented in
the development file (i.e., hdo-edit.owl) within separate, sequentially generated branches, and
4) A merge request is created, and upon approval by at least three contributors, these branches
are merged into the main.
3. Adoption and Adaption: Upon publication of HDO, it will be used in use cases across
the different Helmholtz research fields. This will test the ontology against use case-specific
requirements and allow further adaption based on iterative exchange.
3.1. Reuse of existing terms
We focused on reusing classes from existing, well-known semantic artifacts, wherever possible,
to ensure semantic interoperability. HDO is top-level aligned with the Basic Formal Ontology
(BFO)6 to ensure interoperability with other mid- and domain-level ontologies. Furthermore,
classes and properties from well-known Open Biological and Biomedical Ontologies (OBO)
4
https://codebase.helmholtz.cloud/hmc/hmc-public/hob/hdo
5
https://purls.helmholtz-metadaten.de/hob/HDO_00000000
6
https://obofoundry.org/ontology/bfo
3
Said Fathalla et al. CEUR Workshop Proceedings 1–5
entity continuant generically dependent continuant
occurrent specifically structured data
information
dependent continuant
data
Figure 2: An overview of HDO class hierarchy. Open arrowheads denote subClassOf properties be-
tween the classes. For visualization purpose, certain classes have been excluded from the representation.
were re-used, including: Information Artifact Ontology (IAO)7 (iao:action specification,
iao:information content entity, iao:plan specification) and the Relation On-
tology (RO)8 (ro:input of, ro:has characteristic and ro:has input).
3.2. Concepts overview
An overview of the HDO core classes is shown in Figure 2. HDO establishes semantics for
the core concepts of digital infrastructure, digital information management, and processes
in data management and exchange. For example, HDO offers a rigorous semantic context
for the aspects of the FAIR principles. These concepts are modelled as bfo:disposition
that inhere in hdo:data (HDO_00000009) either according to a practical understanding of
hdo:findability, as well as specified according to the FAIR principles as hdo:findability
according to the FAIR principles. Further, we created classes for different aspects
of metadata standardization under hdo:information and aligned this with IAO classes (e.g.,
iao:information content entity).
3.3. Logic
We asserted pairwise disjointness between mutually disjoint classes, e.g., hdo:digital
infrastructure is disjoint with hdo:hardware. We used bfo:role and bfo:realizes
to create a pattern that allows populating certain classes by inference. For example, the class
hdo:agent is “An entity which realises an agent role”. This is inferred through hdo:agent
7
https://github.com/information-artifact-ontology/IAO/
8
https://obofoundry.org/ontology/ro.html
4
Said Fathalla et al. CEUR Workshop Proceedings 1–5
role and bfo:realizes. A similar example is the class hdo:tool which is defined as “A
continuant which realises a tool role”. For this, as well as to allow reasoning about relevant
concepts, several OWL axioms have been asserted in HDO. The EquivalentClasses axiom
allows to state that several class expressions are equivalent to each other. For instance, the class
hdo:data has the following axiom assertion:
’generically dependent continuant’ and
(’output of’ some (’encoding process’ and (’has input’ some signifier)))
The SubClassOf axiom allows to state that each instance that fits a class expression is
also an instance of that class. For instance, the class hdo:structured vocabulary has the
following SubClassOf axiom assertion:
vocabulary and ’has quality’ some structuredness
4. Conclusions and Future Work
The Helmholtz Digitization Ontology was developed with the objective of providing harmonized
semantics as a reference framework for the Helmholtz digital ecosystem. The development
of HDO was open and transparent, and its full provenance is recorded. Further, we follow an
established ontology development framework (e.g., ODK) and align HDO with well-established
semantic frameworks in order to increase acceptance towards domain-level re-use and ap-
plication. One of the further use cases will be the semantic representation of FAIR digital
objects (FDOs) that will allow data integration between FDOs and the HMC Helmholtz KG
[3]. Such implementations will extend HDO core semantics and facilitate the representation,
interoperability, and analysis of scientific metadata within the Helmholtz digital ecosystem.
References
[1] M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak,
N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al., The FAIR guiding
principles for scientific data management and stewardship, Scientific data 3 (2016).
[2] S. Fathalla, S. Vahdati, C. Lange, S. Auer, Seo: A scientific events data model, in: The
Semantic Web – ISWC 2019, Springer International Publishing, Cham, 2019, pp. 79–95.
[3] J. Bröder, G. Preuß, F. D’Mello, S. Fathalla, V. Hofmann, S. Sandfeld, The Helmholtz
knowledge graph: driving the transition towards a FAIR data ecosystem in the Helmholtz
Association, in: European Semantic Web Conference, Springer, 2024.
[4] N. Matentzoglu, D. Goutte-Gattat, S. Z. K. Tan, J. P. Balhoff, S. Carbon, A. R. Caron, W. D.
Duncan, J. E. Flack, M. Haendel, N. L. Harris, W. R. Hogan, C. T. Hoyt, R. C. Jackson, H. Kim,
H. Kir, M. Larralde, J. A. McMurry, J. A. Overton, B. Peters, C. Pilgrim, R. Stefancsik, S. M.
Robb, S. Toro, N. A. Vasilevsky, R. Walls, C. J. Mungall, D. Osumi-Sutherland, Ontology De-
velopment Kit: a toolkit for building, maintaining and standardizing biomedical ontologies,
Database 2022 (2022).
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