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https://ceur-ws.org/Vol-3830/preface.pdf
Preface: Semantic Industrial Information Modelling
Eduard Kamburjan1 , Ernesto Jimenez-Ruiz1,2 , Baifan Zhou1,3 and Arild Waaler1
1
University of Oslo, Norway
2
City St George’s, University of London, United Kingdom
3
Oslo Metropolitan University, Norway
Information Modelling (IM) has been under the spotlight of both academia and industry
for decades. Important aspects of IM include methods and practices of representing concepts,
relationships, constraints, rules and operations to specify data semantics for a chosen domain
of interest. As a response to the IM challenge a number of modelling paradigms and languages
arose, and they range from ERM, UML, ORM to OWL and Knowledge Graphs and come with a
wide range of systems to support the life cycle of information models.
Despite the past success, existing approaches and systems for IM fail to cope with new
challenges of overwhelming global industrial digitalization that requires advanced information
models and aims at fully computerized, software-driven, automation of production processes
and enterprise-wide integration of software components. Such trend and the technological and
industrial developments that come with it are an important part of Industry 4.0 and industrial
Internet of Things. It requires IM that, for example, allows capturing the functionality of
and information flow between different assets in a plant, such as equipment and production
processes. Moreover, it requires IM and models that are based on ISA and IEC standards and
have a number of desirable properties, e.g., reusable, explainable, scalable, simulatable etc. Such
IM should allow for seamless data sharing and integration e.g., via data marketplaces and across
value chains.
These new challenges require new theory, methodology, best practice, systems and this
should be developed, shared, and discussed by a wire range of stakeholders. In this workshop
we aim at gathering researchers and practitioners who work on addressing these challenges
with the help of semantic technologies. We in particular invite IM experts who are excited
and committed to push the frontiers of IM further and support modern industry in its current
technological transformation. In our workshop we welcome novel methods, systems, solutions,
experience, and practice for semantic industrial information modelling.
In two sessions, the participants discuss semantic information modelling, based on 4 presen-
tation based on the articles in this volume and the invited talk “ Leveraging Simple Semantic
Models and Large Language Models for Event Analysis and Enterprise Data Management” by Oktie
Hassanzadeh, IBM Research . The workshop received 5 submissions, which were all reviewed
by two or three members of the program committee. Four submissions were ultimately accepted.
We thank the program committee and reviewers for their work, and are grateful to the
workshop chairs and reviewers of ISWC’24, who helped with setting up and shaping the event.
SemIIM’24: International Workshop on Semantic Industrial Information Modelling, November 12, 2024, Baltimore, US
Envelope-Open eduard@ifi.uio.no (E. Kamburjan); Ernesto.Jimenez-Ruiz@city.ac.uk (E. Jimenez-Ruiz); baifan.zhou@oslomet.no
(B. Zhou); arild@ifi.uio.no (A. Waaler)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
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Workshop ISSN 1613-0073
Proceedings
Program Committee
• Jiaoyan Chen, University of Mancheter, United Kingdom
• Jieying Chen, Vrije University Amsterdam, Netherlands
• Oscar Corcho, Universidad Politécnica de Madrid, Spain
• Victoria Degeler, University of Amsterdam, Netherlands
• Irlan Grangel Gonzalez, Bosch Corporate Research, Germany
• Andreas Harth, University of Nuremberg, Germany
• Michael Heider, University of Augsburg, Germany
• Dimitrios Kyritsis, EPFL, Switzerland
• André Pomp, University of Wuppertal, Germany
• Dumitru Roman, SINTEF AS / Oslo Metropolitan University, Norway
• Steffen Staab, University of Stuttgart, Germany
• Ahmet Soylu, Oslo Metropolitan University, Norway
• Hideaki Takeda, NII, Japan
• Dirk Walther, DNV, Norway
Additional Reviewers
• Rene Dorsch, University of Nuremberg, Germany
• Ameneh Naghdi Pour, Vrije University Amsterdam, Netherlands
Preface: Software Lifecycle Management for
Knowledge Graphs
David Chaves Fraga1 , Oscar Corcho2 , Eduard Kamburjan3 , Coen De Roover4 and
Paco Nathan5
1
Universidade de Santiago de Compostela, Spain
2
Universidad Politécnica de Madrid, Spain
3
University of Oslo, Norway
4
Vrije Universiteit Brussel, Belgium
5
Derwen.ai, USA
Knowledge graphs are digital artifacts with a complex construction process utilizing numerous
tools and data sources. They are generated in elaborate pipelines utilizing a wide variety of
semantic technologies, for example mapping languages, such as RML or OTTR, or validation
languages, such as SHACL. Further semantic technologies are used to describes the used
ontology, such as OWL, and the adjacent queries, such as SPARQL. Far from a linear process,
multiple data sources must be mapped into the target knowledge graph.
All the involved artifacts, ontologies, mapping scripts, graph shapes, etc., are interdependent
and changes in one of them require the adjustment in others. The building and maintenance
of a knowledge graph needs to apply the artifacts and tools in the correct order in the right
context, e.g., staging and production contexts, as well as manage the intermediate artifacts
generated in substeps. In current practice, managing the dependencies is a manual process and
general management of artifacts and changes is done using ad hoc approaches. Despite the
numerous work on knowledge graph construction, there is a focus on the technical aspects of
the single steps and little attention has been paid to the practical aspects of (a) organizing and
managing knowledge graphs projects in terms of change management, dependencies between
semantic artifacts, as well as DevOps for knowledge graphs, and (b) automating building and
deploying of the resulting knowledge graph and adjacent artifacts. Similarly, connections to
project management in software engineering, where a rich body of experience in DevOps,
building, maintaining and deploying of digital artifacts exists are not systematically explored.
The Software Lifecycle Management for KG workshop (SofLiM4KG) was started to collect
experiences in successful and abandoned knowledge graph projects from this perspective to (a)
carve out the specifics in knowledge graph engineering that pose challenges beyond software
engineering practices, (b) to establish best practices and anti-patterns for the community, and (c)
build the foundations for the systematic investigation of the connection to software engineering,
as well as qualitative and quantitative studies in project management of knowledge graphs.
In two sessions, the participants discusse software for knowledge graphs, based on 3 pre-
sentation and articles in this volume and an invited talk by Thomas Smoke, whyhow.ai . The
SofLiM4KG’24: Software Lifecycle Management for Knowledge Graphs Workshop, November 12, 2024, Baltimore, US
Envelope-Open david.chaves@usc.es (D. Chaves Fraga); oscar.corcho@upm.es (O. Corcho); eduard@ifi.uio.no (E. Kamburjan);
coen.de.roover@vub.be (C. De Roover); paco@derwen.ai (P. Nathan)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
workshop received 4 submissions, which were all reviewed by two or three members of the
program committee. Three submissions were ultimately accepted.
We thank the program committee and reviewers for their work, and are grateful to the
workshop chairs and reviewers of ISWC’24, who helped with setting up and shaping the event.
We also acknowledge the organizers of the Dagstuhl seminar 24061, where the idea for this
workshop arose.
Program Committee
• Wouter Beek, Triply
• Juan F. Sequeda, data.world
• Pieter Colpaert, Ghent University
• Mario Scrocca, CEFRIEL
• Paola Espinoza-Arias, BASF
• Carlos Buil Aranda, Universidad Tecnica Federico Santa Maria
• Umutcan Serles, STI Innsbruck
• Martin G. Skjæveland, University of Oslo
• Richard Bubel, TU Darmstadt
• Romana Pernisch, Vrije Universiteit Amsterdam
• Louis Guitton, unaffiliated
Additional Reviewers
• Arthur Vercruysse, Ghent University