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https://ceur-ws.org/Vol-3647/2023_SemIIM_Preface.pdf
Preface for the 2nd International Workshop on
Semantic Industrial Information Modelling (SemIIM)
Arild Waaler1 , Evgeny Kharlamov2,1 , Baifan Zhou3,1 , Ahmet Soylu3 ,
Dimitrios Kyritsis1 , Dumitru Roman4,3 , Ognjen Savkovic5 and Steffen Staab6
1
SIRIUS Centre, Department of Informatics, University of Oslo, Norway
2
Bosch Center for AI, Germany
3
Department of Computer Science, Oslo Metropolitan University, Norway
4
SINTEF AS, Norway, Norway
5
KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Italy
6
Institute for Artificial Intelligence, University of Stuttgart, Germany
Abstract
SemIIM2023 was a full-day workshop that took place on 7th November 2023 in Athens, Greece, co-
located with the 22nd edition of the International Semantic Web Conference (ISWC2023). This workshop
invited industrial as well as academic keynotes and papers, covering the challenges and solutions for
addressing industrial information modelling, including methods and practices of representing concepts,
relationships, constraints, rules and operations to specify data semantics for a chosen domain of interest.
The workshop gathered the interested community and discussed the latest approaches for challenges
both from the perspectives of academia and industry.
1. Introduction
Information Modelling (IM) has been under the spotlight of both academia and industry for
decades [1, 2]. 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 [3], UML [4], ORM [5] to OWL [6] and Knowledge Graphs [7]
and come with a wide range of systems to support the life cycle of information models [8, 9].
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 [10, 11, 12, 13, 14]. 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 [15]. It requires IM that, for example, allows to capture 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
SemIIM’23: 2nd International Workshop on Semantic Industrial Information Modelling, 7th November 2023, Athens,
Greece, co-located with 22nd International Semantic Web Conference (ISWC 2023)
$ arild@ifi.uio.no (A. Waaler); baifan.zhou@oslomet.no (B. Zhou)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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IEC standards and have a number of desirable properties, e.g., reusable, explainable, scalable,
simulatable etc [16, 17]. Such IM should allow for seamless data sharing and integration e.g.,
via data market places and across value chains [18, 19].
These new challenges require new theory, methodology, best practice, systems and this
should be developed, shared, and discussed by a wire range of stakeholders. Therefore, we
initiated the 1st International Workshop on Semantic Industrial Information Modelling (SemIIM)
to provide a venue for the community of industrial information modelling.
This is the second edition of the workshop. The first edition [20] was held on 30th May 2022,
in Hersonissos, Crete, Greece.
2. The SemIIM2023 Workshop
The 2nd International Workshop on Semantic Industrial Information Modelling (SemIIM) aims at
gathering researchers and practitioners who work on addressing these challenges with the help
of semantic technologies. We in particular invited 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 welcomed novel methods, systems, solutions, experience,
and practice for semantic industrial information modelling.
The workshop included the sessions described in the following sections.
2.1. Academic Keynote
The Academic Keynote was given by Oscar Corcho at Universidad Politécnica de Madrid (Spain)
on the topic: “On the Governance of all the Artefacts used in Knowledge Graph Creation and
Maintenance Scenarios”.
Abstract: The creation and maintenance of knowledge graphs is commonly based on the
generation and use of several types of artefacts, including ontologies, declarative mappings and
different types of scripts and data processing pipelines, sample queries, APIs, etc. All of these
artefacts need to be properly maintained so that knowledge graph creation and maintenance
processes are sustainable over time, especially in those cases where the original data sources
change frequently. It is not uncommon to have situations where ontologies are governed by
an organisation or group of organisations, while mappings and data processing pipelines are
handled by other organisations or individuals, using different sets of principles. This causes
mismatches in the knowledge graphs that are generated, including the need to update all the
associated artefacts (declarative mappings, sample queries, APIs, etc.) so as to keep up to date to
changes in the ontologies, or in the underlying data sources. In this talk we will discuss several
of the challenges associated to the maintenance of all of these artefacts in real-world knowledge
graph scenarios, so as to provide some light into how we could set up a complete knowledge
graph governance model that may be used across projects and initiatives.
2.2. Industrial Keynote
The Industrial Keynote was given by Sten Grüner at ABB Corporate Research (Germany) on
the topic “Asset Administration Shell – Industry 4.0 in theory and practice”.
Abstract: Industry 4.0 is a more than 10-year-old vision of digitalised, adaptive and resilient
industry based on converged IT/OT technologies breaching the gap between engineering and
computer science. Over the year an understanding of gaps between different information do-
mains across product and equipment lifecycle and supply chain emerged. Asset Administration
Shell (AAS) concept was created to close this gap by implementing Digital Twin concepts
for industrial applications. In this talk, I will give a brief introduction into AAS information
model, implementations and present some selected pilots and demonstrators making use of this
technology.
2.3. Workshop Papers
Eleven papers were submitted and accepted. The reviews were hosted at EasyChair. Each
paper received at least three reviews from reviewers with different background and status. The
following papers were accepted for publication and presented at the workshop:
• Automating Building Regulations Conformance Checking Using a Semantic Approach -
Preliminary Results [21]
• Semantic Association Rule Learning from Time Series Data and Knowledge Graphs [22]
• Extending PLASMA for Industrial Semantic Modeling [23]
• GraphGuard: Enhancing Data Quality in Knowledge Graph Pipelines [24]
• The Design of the Diagrammatic and Semantic Models for Process Modelling Language
for Digital Triplet [25]
• SHM: A Light-weight, Mid-level Ontology for Reliable System Health Monitoring [26]
• Multi-Architecture Unified Modeling for Manufacturing Service Value Net System De-
sign [27]
• Term Frequency Analysis for Semantic Modeling of Geological Fault Knowledge in the
Energy Industry [28]
• Towards a Knowledge Graph-based Data Mesh for Smart Manufacturing [29]
• Towards Semantic Modeling of Camera from Image Quality Testing Perspective: Valeo
Vision Systems Case [30]
• Towards Standardised Product Data Exchange with Information Modelling Framework at
Siemens Energy [31]
3. Organising Committee
• Evgeny Kharlamov, Bosch Center for AI / University of Oslo, Germany
• Dimitrios Kyritsis, University of Oslo / EPFL, Norway
• Dumitru Roman, SINTEF AS, Norway
• Ognjen Savkovic, Free University of Bozen-Bolzano, Italy
• Ahmet Soylu, Oslo Metropolitan University, Norway
• Steffen Staab, University of Stuttgart, Germany
• Arild Waaler, University of Oslo, Norway
• Baifan Zhou, Oslo Metropolitan University / University of Oslo, Norway
4. Program Committee
• Jieying Chen, University of Oxford, United Kingdom
• Zhuo Chen, Zhejiang University, China
• Gong Cheng, Nanjing University, China
• Gordian Dziwis, Institut für Angewandte Informatik, Germany
• Christian Glomb, Siemens AG, Germany
• Irlan Grangel-Gonzalez, Bosch Corporate Research, Germany
• Joerg Haehner, University of Augsburg, Germany
• Michael Heider, University of Augsburg, Germany
• Alwin Hoffmann, XITASO GmbH, Germany
• Anton Hummel, XITASO GmbH, Germany
• Eduard Kamburjan, University of Oslo, Norway
• Evgeny Kharlamov, Bosch Center for AI / University of Oslo, Germany
• Dimitrios Kyritsis, University of Oslo / EPFL, Norway
• Qianhang Lyu, University of Oslo / Huazhong University of Science and Technology,
Norway
• Francisco Martin-Recuerda, SINTEF AS, Norway
• Lars-Peter Meyer, Institut für Angewandte Informatik, Germany
• Maja Milicic Brandt, Siemens AG, Germany
• Richard Nordsieck, XITASO GmbH, Germany
• Yuanwei Qu, University of Oslo, Norway
• Dumitru Roman, SINTEF AS, Germany
• Ognjen Savkovic, Free University of Bozen-Bolzano, Italy
• Ahmet Soylu, Oslo Metropolitan University, Norway
• Steffen Staab, University of Stuttgart, Germany
• Arild Waaler, University of Oslo, Norway
• Dirk Walther, DNV, Norway
• Muhammad Yahya, National University of Ireland Galway, Ireland
• Zhuoxun Zheng, Bosch Center for AI, Germany
• Baifan Zhou, Oslo Metropolitan University / University of Oslo, Norway
Acknowledgements
This workshop is supported by the SIRIUS Centre, Norwegian Research Council project number
237898, and the European Commission H2020 projects Dome 4.0 (953163), OntoCommons
(958371), enRichMyData (101070284), and DataCloud (101016835).
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