% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Ihsan:1018782,
author = {Ihsan, Ahmad Zainul},
othercontributors = {Sandfeld, Stefan and Kerzel, Ulrich Bernd},
title = {{E}nabling the digital transformation in materials science
and engineering: leveraging ontologies for knowledge
representation, provenance, and text mining},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-07969},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {The digital transformation of Materials Science and
Engineering (MSE) is essential for accelerating the
development of novel materials and enhancing understanding
of the materials life cycle, encompassing raw resources,
functional materials, engineered components, and beyond.
This transformation entails integrating computational
methods, data science, and artificial intelligence (AI) to
advance the field of MSE. However, the heterogeneity of data
formats, unstructured information, and the reproducibility
crisis in MSE pose challenges to the effective management,
reuse, and analysis of data. This thesis addresses these
challenges by leveraging ontologies as the foundation for
semantic data enrichment, facilitating knowledge
representation, provenance documentation, and text mining.
The first contribution of this work is the development of
the Dislocation Ontology (DISO), an ontology that represents
the domain knowledge of linear defects in crystalline
materials. The development of DISO was driven by the
objective of facilitating data interoperability with other
MSE-related data. DISO was aligned with the Elementary
Multiperspective Material Ontology (EMMO) and Materials
Design Ontology (MDO) to ensure interoperability. The
ontology alignment efficiently represents the dislocation
simulation data. Moreover, we present a real-world use case
of representing discrete dislocation dynamics data as a
knowledge graph (DisLocKG), which can depict the
relationships between them. Additionally, DisLocKG is
accessible, as we developed a SPARQL endpoint that offers
considerable flexibility when querying DisLocKG. Another
contribution of this work is the PRovenance Information for
MAterials Science (PRIMA) ontology, which was designed to
document provenance information in MSE research, promoting
data reliability, trustworthiness, and reproducibility.
PRIMA was aligned with the Provenance Ontology (PROV-O) and
the Platform Material Digital core ontology (PMDco) and was
evaluated through use cases involving metallic biomaterial
fabrication and microscopy data. Furthermore, this thesis
presents a framework integrating Large Language Models
(LLMs) and Semantic Web technologies to extract structured
data from unstructured materials synthesis text. Free-text
data is transformed into machine-readable formats such as
JSON and further enriched semantically using an ontology.
The overarching objective of this work is to demonstrate an
interdisciplinary approach that integrates MSE knowledge,
Semantic Web technologies, and Natural Language Processing
(NLP) to facilitate the digital transformation of MSE. The
developed ontologies and knowledge graphs are pivotal in
data enrichment and interoperability, ensuring that
materials data adhere to the FAIR (Findable, Accessible,
Interoperable, Reusable) data principles. The LLMs-based
text mining framework provides a strategy for handling
unstructured data in materials synthesis-related text,
enabling the generation of linked data and knowledge
extraction. Despite these advances, challenges persist in
expanding DISO and DisLocKG to new use cases, refining data
models, and improving long-context processing in LLMs.
Future works will entail the development of Application
Programming Interfaces (APIs) for DisLocKG, extending PRIMA
with computational modules, and exploring more efficient
LLMs architectures for a more extensive range of text-mining
applications. We envision a future where Semantic Web
technologies and AI converge to enable machines to extract,
process, and understand scientific data, ultimately driving
the digital transformation in MSE.},
cin = {527210 / 520000},
ddc = {620},
cid = {$I:(DE-82)527210_20201120$ / $I:(DE-82)520000_20140620$},
pnm = {MuDiLingo - A Multiscale Dislocation Language for
Data-Driven Materials Science (759419)},
pid = {G:(EU-Grant)759419},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-07969},
url = {https://publications.rwth-aachen.de/record/1018782},
}
guest ::