Latest publications – SIRIUS

• Gandhi, Dharani; May, Gokan & Giannakopoulos, Foivos Psarommatis (2026). An Integrated RFID and Vision-Based Closed-Loop Quality Control Architecture for Sterile Medical Device Assembly Lines: Industrial Implementation and Validation. Applied Sciences. 16(10). doi: 10.3390/app16104841. Full text in Research Archive
• Pakala, Harish Kumar; Wiesmayr, Bianca; Diedrich, Christian & Cameron, David B. (2026). A DSL for Integrating Engineering Artifacts and Behavior into the Asset Administration Shell. Scitepress. ISSN 2184-4348. 1, p. 135–149. doi: 10.5220/0014634700004058. Full text in Research Archive Show summary

  Discrete and process automation (DPA) systems span multiple engineering domains. They also evolve across different stages of the system life cycle, which leads to semantic heterogeneity and challenges for model-based systems engineering (MBSE). The Asset Administration Shell (AAS) defines a generic meta-model that enables semantic annotation via external vocabularies for information representation. However, AAS primarily supports static, hierarchical representations and lacks dynamic and behavioral constructs needed to capture workflows, orchestration, and batch production strategies. In this regard, this paper introduces SAFSL, the Semantically Annotated Functional Specification Language, which extends the AAS meta-model with dynamic and behavioral abstractions, thus, supporting modular, reusable, and semantically enriched specifications. SAFSL enables assume-guarantee reasoning and provides a foundation for automatic code generation, thereby reducing errors and improving consistenc y in DPA system modeling. The approach is demonstrated through a batch process plant case study, highlighting a) semantic integration of DEXPI, the digital P&ID representation, with AAS submodel templates, b) functional specification of batch-phase operations, c) the need for integrating electrical CAD diagrams with AAS submodel templates, and d) potential connections between DEXPI and ECAD representations. These insights reveal open challenges in bridging semantic gaps across heterogeneous submodel templates while illustrating the applicability of SAFSL for MBSE of DPA systems.

• Mehmandarov, Rustam & Waaler, Arild Torolv Søetorp (2025). A Framework for Semantic Identifier Resolution and LLM Grounding in Engineering Systems. CEUR Workshop Proceedings. 4064. doi: https:/ceur-ws.org/Vol-4064/. Full text in Research Archive Show summary

  The digital transformation of engineering systems demands scalable and precise identifier management. Information about a single asset is often fragmented across numerous systems and organizational boundaries, with each context using its own identifiers. This paper addresses this challenge by introducing a formal framework for semantic reference. We build on prior work by introducing two key concepts: reference contexts, which formalize the boundary conditions for identifier interpretation, and public models, which serve as curated, shared layers for anchoring reference. We define reference equality as the symmetric, transitive closure over typed proxy relations that link identifiers across these contexts. Finally, we demonstrate how this semantic infrastructure provides a foundational component for a new generation of industrial systems, including providing essential grounding for Large Language Model (LLM) workflows. This approach bridges the gap between human-readable descriptors and machine-readable identifiers, aligns with modern architectural principles like Data Mesh, and supports hybrid reasoning in industrial knowledge systems.

• Giannakopoulos, Foivos Psarommatis; May, Gokan & Azamfirei, Victor (2025). Product reuse and repurpose in circular manufacturing: a critical review of key challenges, shortcomings and future directions. Journal of Remanufacturing. ISSN 2210-464X. 15, p. 273–310. doi: 10.1007/s13243-025-00153-y. Full text in Research Archive Show summary

  This paper explores the transformative potential of reuse and repurposing strategies in the manufacturing industry, with a focus on sustainability and circular economy principles. Through an extensive review of 115 articles from Scopus using 6 different queries for broad topic coverage, the study provides a comprehensive analysis of reuse and repurposing frameworks, highlighting their benefits and challenges. The research identifies three essential pillars for successful implementation: product design, operational processes, and consumer acceptance. The study emphasizes the significance of modular designs, lifecycle extensions, and advanced cost models to maximize resource efficiency and sustainability. The paper also discusses the integration of advanced technologies, such as blockchain, artificial intelligence, and IoT-enabled systems, to enhance traceability, streamline reverse logistics, and optimize predictive maintenance. Automated disassembly and collaborative robotics are highlighted as critical enablers for efficient and scalable reuse operations. Furthermore, the study advocates for flexible supply chains and real-time data analytics to address uncertainties in core acquisition and product variability. Despite the progress, the lack of standardized frameworks and metrics remains a significant barrier to widespread adoption. The paper concludes by proposing a cohesive framework that incorporates technological advancements, regulatory support, and collaborative approaches to overcome these limitations. This study provides actionable insights to foster a scalable, transparent, and sustainable manufacturing ecosystem.

• Cameron, David B.; Otten, Wilhelm; Temmen, Heiner; Hole, Monica & Tolksdorf, Gregor (2024). DEXPI process: Standardizing interoperable information for process design and analysis. Computers and Chemical Engineering. ISSN 0098-1354. 182. doi: 10.1016/j.compchemeng.2023.108564. Full text in Research Archive Show summary

  DEXPI Process is a proposed standard for modelling information about process design, as it is presented on block flow and process flow diagrams. It was developed by the DEXPI+ working group and builds upon the DEXPI (Data Exchange in the Process Industry) standard for piping and instrumentation diagrams. Digitalization is making increasing demands on the exchange of information in the process facility lifecycle. Industry 4.0 methods require shared terminology and knowledge models to exchange information. Standards, such as ISO15926, CFIHOS and DEXPI, try to address this need. All these focus on the physical plant items, as shown on a PID or 3D model. There is a lack of standards for early-phase, top-down process design. DEXPI Process fills this gap. This paper presents the development of DEXPI Process in the context of knowledge modelling of process systems and previews how the model is a foundation for applications of automated reasoning and decision support for design and operations.

• Qu, Yuanwei; Kamburjan, Eduard; Torabi, Anita & Giese, Martin (2024). Semantically triggered qualitative simulation of a geological process. Applied Computing and Geosciences. 21. doi: 10.1016/j.acags.2023.100152. Full text in Research Archive
• Giannakopoulos, Foivos Psarommatis & May, Gokan (2024). Optimization of zero defect manufacturing strategies: A comparative study on simplified modeling approaches for enhanced efficiency and accuracy. Computers & industrial engineering. ISSN 0360-8352. 187. doi: 10.1016/j.cie.2023.109783. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Yang, Hui; Tan, Zhipeng; Waaler, Arild Torolv Søetorp & Kharlamov, Evgeny [Show all 7 contributors for this article] (2024). Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. Full text in Research Archive
• Bobed, Carlos; Bobillo, Fernando; Jimenez-Ruiz, Ernesto; Mena, Eduardo & Pan, Jeff Z. (2024). Praedixi, Redegi, Cogitavi: Adaptive knowledge for resource-aware semantic reasoning. Expert Systems With Applications. ISSN 0957-4174. 250. doi: 10.1016/j.eswa.2024.123838. Full text in Research Archive
• Chen, Qiaosheng; Luo, Weiqing; Huang, Zixian; Lin, Tengteng; Wang, Xiaxia & Soylu, Ahmet [Show all 10 contributors for this article] (2024). ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries. In Yang, Grace Hui; Wang, Hongning; Han, Sam; Hauff, Claudia; Zuccon, Guido & Zhang, Yi (Ed.), SIGIR '24 : Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery (ACM). ISSN 9798400704314. p. 303–312. Full text in Research Archive
• Giannakopoulos, Foivos Psarommatis; Zhou, Baifan & Kharlamov, Evgeny (2024). Implementation of Zero Defect Manufacturing using quality prediction: a spot welding case study from Bosch. Procedia Computer Science. 232, p. 1299–1308. doi: 10.1016/j.procs.2024.01.128. Full text in Research Archive Show summary

  Product quality is a vital aspect in the operation of manufacturing systems, manufacturers need to implement at least one quality assurance method to assure the desired quality. The most recent approach for quality assurance is named Zero Defect Manufacturing (ZDM). The scope of the current paper is the implementation of ZDM approach in the automotive industry and specifically for the spot-welding process. Using a machine learning method that is utilizing linear regression and LSTM and consuming data from the production such as sensors and other engineering data to predict the quality of future spot welds. Using this quality prediction there is a root cause analysis to identify why the spot weld will fail and the appropriate prevention actions are proposed. The next step is the training and validation of the machine learning model and the calculation of the accuracy of the model. Once the accuracy of the model is validated a series of simulations, using a dynamic scheduling tool, are performed in order to calculate a series of KPIs to evaluate the impact of the proposed method to the production.

• Zheng, Zhuoxun; Zhou, Baifan; Yang, Hui; Tan, Zhipeng; Sun, Zequn & Li, Chunnong [Show all 9 contributors for this article] (2024). Knowledge graph embedding closed under composition. Data mining and knowledge discovery. ISSN 1384-5810. 38. doi: 10.1007/s10618-024-01050-x. Full text in Research Archive Show summary

  Knowledge Graph Embedding (KGE) has attracted increasing attention. Relation patterns, such as symmetry and inversion, have received considerable focus. Among them, composition patterns are particularly important, as they involve nearly all relations in KGs. However, prior KGE approaches often consider relations to be compositional only if they are well-represented in the training data. Consequently, it can lead to performance degradation, especially for under-represented composition patterns. To this end, we propose HolmE, a general form of KGE with its relation embedding space closed under composition, namely that the composition of any two given relation embeddings remains within the embedding space. This property ensures that every relation embedding can compose, or be composed by other relation embeddings. It enhances HolmE’s capability to model under-represented (also called long-tail) composition patterns with limited learning instances. To our best knowledge, our work is pioneering in discussing KGE with this property of being closed under composition. We provide detailed theoretical proof and extensive experiments to demonstrate the notable advantages of HolmE in modelling composition patterns, particularly for long-tail patterns. Our results also highlight HolmE’s effectiveness in extrapolating to unseen relations through composition and its state-of-the-art performance on benchmark datasets.

• Chen, Jiaoyan; He, Yuan; Geng, Yuxia; Jimenez-Ruiz, Ernesto; Dong, Hang & Horrocks, Ian (2023). Contextual semantic embeddings for ontology subsumption prediction. World wide web (Bussum). ISSN 1386-145X. doi: 10.1007/s11280-023-01169-9. Full text in Research Archive
• Giannakopoulos, Foivos Psarommatis & May, Gökan (2023). A Systematic Analysis for Mapping Product-Oriented and Process-Oriented Zero-Defect Manufacturing (ZDM) in the Industry 4.0 Era. Sustainability. 15(16). doi: 10.3390/su151612251. Full text in Research Archive
• Wang, Xiaxia; Cheng, Gong; Pan, Jeff Z.; Kharlamov, Evgeny & Qu, Yuzhong (2023). BANDAR: Benchmarking Snippet Generation Algorithms for (RDF) Dataset Search. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(2), p. 1227–1241. doi: 10.1109/TKDE.2021.3095309. Full text in Research Archive
• Klironomos, Antonis; Zhou, Baifan; Zhipeng, Tan; Zhuoxun, Zheng; Mohamed, H. Gad-Elrab & Paulheim, Heiko [Show all 7 contributors for this article] (2023). ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13998. Full text in Research Archive
• Giannakopoulos, Foivos Psarommatis; May, Gokan; Azamfirei, Victor; Magnanini, Maria Chiara & Powell, Daryl (2023). A Readiness Level Assessment Framework for Zero Defect Manufacturing (ZDM). In Silva, Francisco J. G.; Ferreira, Luís Pinto; Sá, José Carlos; Pereira, Maria Teresa & Pinto, Carla M. A. (Ed.), Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems: Proceedings of FAIM 2023, June 18–22, 2023, Porto, Portugal, Volume 2: Industrial Management. Springer. ISSN 9783031381652. p. 451–459. doi: 10.1007/978-3-031-38165-2_53. Full text in Research Archive
• Rincon-Yanez, Diego; Gad-Elrab, Mohamed H.; Stepanova, Daria; Tran, Kien Trung; Xuan, Cuong Chu & Zhou, Baifan [Show all 7 contributors for this article] (2023). Addressing the Scalability Bottleneck of Semantic Technologies at Bosch. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13998. Full text in Research Archive
• Calhau, Rodrigo Fernandes; Kokkula, Satyanarayana (Satya); Cameron, David B.; Guizzardi, Giancarlo & Almeida, João Paulo A. (2023). Modeling Competence Framework Elements with an Ontology-based Approach. IEEE Conference on Business Informatics (CBI). ISSN 2378-1963. 25, p. 17–26. doi: 10.1109/CBI58679.2023.10187498. Full text in Research Archive Show summary

  Organizations are required to pay constant attention to human resource development in order to prosper. Competence frameworks have received increased attention in this context, as the availability of qualified people with the right combination of competences establishes itself as a major issue for organizational performance. This paper investigates the modeling of competence frameworks in Enterprise Architecture: (i) we identify a key set of competence-related concepts found in competence frameworks; (ii) analyze them using the Unified Foundational Ontology to build a reference Competence Ontology, and, then; (iii) propose well-founded representation patterns for competence modeling in the ArchiMate EA language, discussing how these patterns can be embedded in enterprise competence-based practices.

• Kamburjan, Eduard; Scaletta, Marco & Rollshausen, Nils (2023). Deductive verification of active objects with Crowbar. Science of Computer Programming. ISSN 0167-6423. 226. doi: 10.1016/j.scico.2023.102928. Full text in Research Archive
• Qu, Yuanwei; Perrin, Michel; Torabi, Anita; Abel, Mara & Giese, Martin (2023). GeoFault: A well-founded fault ontology for interoperability in geological modeling. Computers & Geosciences. ISSN 0098-3004. 182. doi: 10.1016/j.cageo.2023.105478. Full text in Research Archive Show summary

  Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general level. Therefore, domain ontologies that describe geological knowledge must be based on a sound ontological background to ensure this knowledge is integrable. Faults are essential for understanding and solving structural problems but are complex to model because the concept of fault includes a group of geological entities with a distinct ontological nature. A fault can correspond to thin, deformed rock volumes or spatial arrangements resulting from the displacement of geological blocks, but at a broader scale, geologists describe faults as surfaces or components of complex fault arrays. Our work intends to harmonize these views by presenting a domain ontology, GeoFault, resting on the Basic Formal Ontology (BFO) and the GeoCore ontology. GeoCore and GeoFault support the parametric description of geological sites as a preliminary step for quantitative and qualitative analysis. We have proposed GeoFault after systematically revising the literature and several knowledge-acquisition sessions with expert structural geologists. The ontology formalizes a vocabulary for fault “sensu stricto,” excluding ductile shear deformations. It covers the regional to outcrop scales, excluding structures at the microscopic, orogenic, and tectonics scales, and it avoids interpretive language associated with geological processes as far as possible. Extending the BFO and GeoCore ontologies allows the fault concept to be related to formal ontological classes in a consistent semantic-rich framework. The ontology artifact is implemented in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The GeoFault ontology is publicly available and provides a solid framework for clarifying fault knowledge and a foundation for many applications.

• Azamfirei, Victor; Giannakopoulos, Foivos Psarommatis & Lagrosen, Yvonne (2023). Application of automation for in-line quality inspection, a zero-defect manufacturing approach. Journal of manufacturing systems. ISSN 0278-6125. 67, p. 1–22. doi: 10.1016/j.jmsy.2022.12.010. Full text in Research Archive Show summary

  Contemporary manufacturing must prioritise the sustainability of its manufacturing processes and systems. Zero Defect Manufacturing (ZDM) focusses on minimising waste of any kind using data-driven technology, hence enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, the application of automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time, and thus adapt the system to disturbances. In this work, we provide a systematic review of the literature on current trends in the application of automation for in-line quality inspection with the ultimate objective of achieving ZDM. Additionally, bibliometric and performance analyses have been performed to gain a complete picture of the field. In this work, we have collected bibliometric data from the most widely referred search engines for academic engineering papers, i.e. Scopus, Web of Science, and IEEE Explorer, involving a total of 145 academic publications from 2011 to 2021. Uniquely for this study, we used three research attributes for the analysis of the selected articles, that is, the level of automation, the condition for quality inspection, and the contribution to ZDM dimensions. The literature suggests that there is a lack of research on the use of in-line detection data for the prediction of defects or repair. Based on the results and our interpretation of the literature, an adapted framework of ZDM (Psarommatis et al., 2020a) and multi-layer quality inspection (Azamfirei et al., 2021a) is presented.

• Zhang, Fanjin; Liu, Xiao; Tang, Jie; Dong, Yuxiao; Yao, Peiran & Zhang, Jie [Show all 12 contributors for this article] (2023). OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(9), p. 9225–9239. doi: 10.1109/TKDE.2022.3222168. Full text in Research Archive
• Korotkova, Nataliia; Benders, Josephus Gerardus; Mikalef, Patrik & Cameron, David (2023). Maneuvering between skepticism and optimism about hyped technologies: Building trust in digital twins. Information & Management. ISSN 0378-7206. 60(4). doi: 10.1016/j.im.2023.103787. Full text in Research Archive Show summary

  IT vendors’ promises are likely to meet sound skepticism from prospective clients. If a particular technology is in vogue and seen as a “hype,” clients are under pressure to buy the technology while, at the same time, skepticism about its claimed benefits might be reinforced. Finding a balance between optimism and skepticism is essential. In this qualitative case study, we examine how an oil and gas supplier company in Norway deals with different pressures when adopting and subsequently implementing digital twin (DT) technologies. DTs offer the promise of creating a digital representation of the physical assets that can keep production facilities operating efficiently and optimally and, as such, have been heralded as enabling the next frontier of productivity improvements. Our results reveal a set of different pressures promoting the decision to adopt the hyped technology. Yet, when descended to the local context, the hype status of DTs evokes multilevel perception segmentation that hype interpreters maneuver by building trust. Based on this analysis, we propose a framework of trust-building mechanisms that contribute to a more nuanced understanding of adoption of hyped technologies and enable practitioners to deal with hype-induced perception obstacles.

• Brandt, Maja Miličić; Psarommatis, Foivos; Simon, Christophe; Waaler, Arild Torolv Søetorp & Zhou, Baifan (2023). Towards Standardised Product Data Exchange with Information Modelling Framework at Siemens Energy. CEUR Workshop Proceedings. 3647. Full text in Research Archive
• Giannakopoulos, Foivos Psarommatis; May, Gökan & Azamfirei, Victor (2023). Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of manufacturing systems. ISSN 0278-6125. 68, p. 376–399. doi: 10.1016/j.jmsy.2023.04.009. Full text in Research Archive Show summary

  To provide direction and advice for future research on Industry 4.0 maintenance, we conducted a comprehensive analysis of 344 eligible journal papers published between 2013 and 2022. Our systematic literature review identifies key trends in advanced maintenance techniques and the consolidation of traditional maintenance concepts, which are driven by the increasing adoption of Industry 4.0 technologies and the need to optimize manufacturing systems' performance and reliability. In light of our findings, we highlight the importance of addressing sustainability factors, human aspects, and the implementation of environmental KPIs in future research. Building upon these insights, we introduce the Maintenance 5.0 framework, which emphasizes the integration of human-centered and AI-driven strategies for achieving efficient and sustainable maintenance in Zero-Defect Manufacturing (ZDM) systems. We propose a novel framework that links traditional and advanced maintenance policies for small and medium-sized enterprises (SMEs) to facilitate the adoption of Industry 4.0 technologies in the maintenance field. This work underscores the need for future research to bridge the gap between these policies, enabling a seamless transition for SMEs towards Industry 4.0 maintenance practices, while fostering sustainable and socially responsible operations.

• Giannakopoulos, Foivos Psarommatis; Fraile, Francisco & Ameri, Farhad (2023). Zero Defect Manufacturing ontology: A preliminary version based on standardized terms. Computers in industry (Print). ISSN 0166-3615. 145. doi: 10.1016/j.compind.2022.103832. Full text in Research Archive
• Tan, Zhipeng; Zhou, Baifan; Zheng, Zhuoxun; Savkovic, Ognjen; Huang, Ziqiang & Gonzalez, Irlan-Grangel [Show all 8 contributors for this article] (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14266, p. 453–471. doi: 10.1007/978-3-031-47243-5_25. Full text in Research Archive Show summary

  Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.

• Zhou, Baifan; Nikolov, Nikolay Vladimirov; Zheng, Zhuoxun; Luo, Xianghui; Savkovic, Ognjen & Roman, Dumitru [Show all 8 contributors for this article] (2023). Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14266, p. 380–399. doi: 10.1007/978-3-031-47243-5_21. Full text in Research Archive Show summary

  Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.

• Tan, Zhipeng; Zheng, Zhuoxun; Klironomos, Antonis; Gad-Elrab, Mohamed H.; Xiao, Guohui & Soylu, Ahmet [Show all 8 contributors for this article] (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring. CEUR Workshop Proceedings. 3632. Full text in Research Archive Show summary

  Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem, that is quality monitoring for welding in manufacturing industry. It is an important process accounting for production of millions of cars annually. The work is in line with our research of data-driven solutions that intends to replace the traditional costly quality monitoring. The paper tackles two challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods with literals on real industry data, with consideration of literals. This paper accompanies the full paper in in-use track and provides additional discussion on problem formulation, literal handling strategies, and included information in industrial KG construction.

• Zheng, Zhuoxun; Savkovic, Ognjen; Nikolov, Nikolay Vladimirov; Luu, Huu Phuc; Soylu, Ahmet & Kharlamov, Evgeny [Show all 7 contributors for this article] (2023). Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration. CEUR Workshop Proceedings. 3632. doi: https:/ceur-ws.org/Vol-3632/ISWC2023_paper_480.pdf. Full text in Research Archive Show summary

  Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. To this end, we propose SemCloud, a semantics-enhanced cloud system, for tackling the challenges of data volume and more users. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results. This poster paper accompanies our full paper and focuses on Datalog rules with external machine learning functions for automated resource configuration, and provides additional discussion on formalism and implementation techniques.

• Zheng, Zhuoxun; Zhou, Baifan; Tan, Zhipeng; Savkovic, Ognjen; Rincon-Yanez, Diego & Nikolov, Nikolay Vladimirov [Show all 9 contributors for this article] (2023). Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch. CEUR Workshop Proceedings. 3632. doi: https:/ceur-ws.org/Vol-3632/. Full text in Research Archive Show summary

  Background and Challenges. Industry 4.0 focuses on smart factories that rely on IoT tech- nology for automation. This produces massive amounts of production data, increasing the demand for data-driven solutions and cloud technology. Yet, users of these solutions and cloud technology are often not cloud experts, such as domain experts and data scientists (Fig. 1.1). In a standard setting of a data science project, the team requires extensive assistance from cloud experts, whenever they want to deploy solutions or make small changes to their solutions deployed on the cloud. To facilitate the adoption of cloud systems careful planing to balance cost and benefits is required. Scaling data science solutions presents challenges of handling high data volume and enabling a broader users which are non-cloud experts to use cloud systems.

• Qu, Yuanwei; Zhou, Baifan; Waaler, Arild Torolv Søetorp & Cameron, David B. (2023). Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry. In Liu, Fenrong; Sadanandan, Arun Anand; Pham, Duc Nghia; Mursanto, Petrus & Lukose, Dickson (Ed.), 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023 (PRICAI 2023): Trends in Artificial Intelligence. Springer. ISSN 9789819970247. p. 466–473. doi: 10.1007/978-981-99-7025-4_41. Full text in Research Archive Show summary

  The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired production events, can cause severe environmental and economic damage. Previous studies have investigated machine learning (ML) methods for undesired event detection. However, the prediction of event probability in real-time was insufficiently addressed, which is essential since it is important to undertake early intervention when an event is expected to happen. This paper proposes two ML approaches, random forests and temporal convolutional networks, to detect undesired events in real-time. Results show that our approaches can effectively classify event types and predict the probability of their appearance, addressing the challenges uncovered in previous studies and providing a more effective solution for failure event management during the production.

• Chen, Bo; Zhang, Jing; Zhang, Xiaokang; Dong, Yuxiao; Song, Jian & Zhang, Peng [Show all 9 contributors for this article] (2023). GCCAD: Graph Contrastive Coding for Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(8), p. 8037–8051. doi: 10.1109/TKDE.2022.3200459. Full text in Research Archive
• Yahya, Muhammad; Zhou, Baifan; Breslin, John G.; Ali, Muhammad Intizar & Kharlamov, Evgeny (2023). Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry. IEEE Access. 11, p. 37360–37377. doi: 10.1109/ACCESS.2023.3267000. Full text in Research Archive Show summary

  The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, etc. Automatic welding, a widely applied manufacturing process in these domains, is not an exception to these changes. One type of automatic welding, Resistance Spot Welding (RSW), lies at the center of this work. Large volumes and varieties of RSW data are being generated, thanks to the technologies behind I4.0. To address the associated data challenges, ontologies are essential in various aspects: integrating data sources, enhancing interoperability, and unifying knowledge etc. However, there have been limited studies around the semantic modelling of Resistance Spot Welding: Existing ontologies have overlooked some crucial concepts, such as an operation-centric view, welding software, and welding electrodes, which are essential for the monitoring of sensor measurements as well as the status of machine components (e.g., electrode wear). Additionally, current ontologies are not publicly available (to the best of our knowledge), and therefore cannot be accessed by other users. Such a lack of availability often requires that users build their ontologies from scratch. In this paper, we propose our RSW ontology (RSWO) (RSWO is publicly available at https://w3id.org/def/mo-rswo) to formalize knowledge in the RSW domain. It combines three sources of knowledge: extensive discussions with Bosch welding experts; reusing terminologies following ISO-14327 and ISO-14373 standards; and existing established ontologies. We have evaluated RSWO on real-world data from monitoring welding quality at Bosch in Germany, using Competency Questions, FAIR principles, OOPS!, and OntoMetrics.

• Stenseth, Nils Christian; Schlatte, Rudolf; Liu, Xiaoli; Pielke, Roger; Li, Ruiyun & Chen, Bin [Show all 18 contributors for this article] (2023). How to avoid a local epidemic becoming a global pandemic. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. 120(10). doi: 10.1073/pnas.2220080120. Full text in Research Archive Show summary

  Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

• Qu, Yuanwei; Kamburjan, Eduard & Giese, Martin (2023). A Geological Case Study on Semantically Triggered Processes. In Pesquita, Catia; Jimenez-Ruiz, Ernesto; McCusker, Jamie; Faria, Daniel; Dragoni, Mauro; Dimou, Anastasia; Troncy, Raphaël & Hertling, Sven (Ed.), The Semantic Web: 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28–June 1, 2023, Proceedings. Springer. ISSN 9783031334559. Full text in Research Archive
• May, Gokan & Giannakopoulos, Foivos Psarommatis (2023). Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research. Energies. 16(10). doi: 10.3390/en16104179. Full text in Research Archive
• Fraile, Francisco; Giannakopoulos, Foivos Psarommatis; Alarcón, Faustino & Joan, Jordi (2023). A Methodological Framework for Designing Personalised Training Programs to Support Personnel Upskilling in Industry 5.0. Computers. 12(11). doi: 10.3390/computers12110224. Full text in Research Archive
• Calhau, Rodrigo Fernandes; Sales, Tiago Prince; Oliveira, Ítalo; Kokkula, Satyanarayana (Satya); Pires, Luís Ferreira & Cameron, David B. [Show all 8 contributors for this article] (2023). A System Core Ontology for Capability Emergence Modeling. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14367, p. 3–20. doi: 10.1007/978-3-031-46587-1_1. Full text in Research Archive Show summary

  To properly understand organizational adaptation and innovation, it is critical to understand the emergence phenomenon, i.e., how the capabilities of a system emerge after changes. However, for this, we should be able to explain systems, their structure, behavior, and capabilities. In pursuit of an understanding of the emergence phenomenon and the nature of those new kinds of systems in organizations, we propose a well-founded system core ontology based on the Unified Foundational Ontology. The ontology is also grounded in system science definitions and disposition theories. For a more integrated explanation of emergence, the proposed ontology considers distinct perspectives of a system, such as its composition, structure, properties, and functions. In the end, we discuss the applications and implications of the proposed ontology on the enterprise architecture area and emergence modeling.

• Sanchez-Graillet, Olivia; Witte, Christian; Grimm, Frank; Grautoff, Steffen; Ell, Basil & Cimiano, Philipp (2022). Synthesizing evidence from clinical trials with dynamic interactive argument trees. Journal of Biomedical Semantics. 13(1). doi: 10.1186/s13326-022-00270-8. Full text in Research Archive Show summary

  Background Evidence-based medicine propagates that medical/clinical decisions are made by taking into account high-quality evidence, most notably in the form of randomized clinical trials. Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach –by means of systematic reviews– a conclusive recommendation on which treatment is best suited for a given patient population. However, it is challenging to produce systematic reviews to keep up with the ever-growing number of published clinical trials. Therefore, new computational approaches are necessary to support the creation of systematic reviews that include the most up-to-date evidence.We propose a method to synthesize the evidence available in clinical trials in an ad-hoc and on-demand manner by automatically arranging such evidence in the form of a hierarchical argument that recommends a therapy as being superior to some other therapy along a number of key dimensions corresponding to the clinical endpoints of interest. The method has also been implemented as a web tool that allows users to explore the effects of excluding different points of evidence, and indicating relative preferences on the endpoints. Results Through two use cases, our method was shown to be able to generate conclusions similar to the ones of published systematic reviews. To evaluate our method implemented as a web tool, we carried out a survey and usability analysis with medical professionals. The results show that the tool was perceived as being valuable, acknowledging its potential to inform clinical decision-making and to complement the information from existing medical guidelines. Conclusions The method presented is a simple but yet effective argumentation-based method that contributes to support the synthesis of clinical trial evidence. A current limitation of the method is that it relies on a manually populated knowledge base. This problem could be alleviated by deploying natural language processing methods to extract the relevant information from publications.

• Zhou, Baifan; Pychynski, Tim; Reischl, Markus; Kharlamov, Evgeny & Mikut, Ralf (2022). Machine Learning with Domain Knowledge for Predictive Quality Monitoring in Resistance Spot Welding. Journal of Intelligent Manufacturing. ISSN 0956-5515. 33, p. 1139–1163. doi: 10.1007/s10845-021-01892-y. Full text in Research Archive Show summary

  Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance SpotWelding (RSW). Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

• Różańska, Marta; Skrzypek, Paweł; Materka, Katarzyna & Horn, Geir (2022). An Architecture for Autonomous Proactive and Polymorphic Optimization of Cloud Applications. In Barolli, Leonard; Hussain, Farookh & Enokido, Tomoya (Ed.), Advanced Information Networking and Applications: Proceedings of the 36th International Conference on Advanced Information Networking and Applications (AINA-2022), Volume 3. Springer. ISSN 9783030996185. p. 567–577. doi: 10.1007/978-3-030-99619-2_53. Full text in Research Archive Show summary

  Existing autonomous Cloud application management platforms continuously monitor the load and the environment of the application react when optimization is needed. This paper introduces the concepts of polymorphic architecture optimization and proactive adaptation of Cloud applications, which is a significant improvement of the standard reactive optimization. Polymorphic architecture optimization considers the change of the technical form of the component while proactive adaptation uses the predicted future workload and context. Based on this, we propose an architecture for a Cross-Cloud application management platform that supports complex optimization of Cloud applications in terms of architecture, resources, and available offers.

• Horn, Geir; Schlatte, Rudolf & Johnsen, Einar Broch (2022). Digital Twins for Autonomic Cloud Application Management. In Barolli, Leonard; Hussain, Farookh & Enokido, Tomoya (Ed.), Advanced Information Networking and Applications: Proceedings of the 36th International Conference on Advanced Information Networking and Applications (AINA-2022), Volume 3. Springer. ISSN 9783030996185. p. 141–152. doi: 10.1007/978-3-030-99619-2_14. Full text in Research Archive Show summary

  Cloud applications are distributed in nature, and it is challenging to orchestrate an application across different Cloud providers and for the different capabilities along the Cloud continuum, from the centralized data centers to the edge of the network. Furthermore, optimal dynamic reconfiguration of an application often takes more time than available at runtime. The approach presented in this paper uses a concurrent simulation model of the application that is continuously updated with real-time monitoring data, optimizing, and validating deployment reconfiguration decisions prior to enacting them for the running applications. This enables proactive decisions to be taken for a future time point, thereby allowing ample time for the reconfiguration actions, as well as realistic Bayesian estimation of the application’s time variate operational parameters for the optimization process.

• Schlatte, Rudolf (2022). A Note on Idleness Detection of Actor Systems. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13360, p. 437–445. doi: 10.1007/978-3-031-08166-8_21. Full text in Research Archive Show summary

  In an actor system, detecting global idleness is trivial if the global state can be inspected synchronously. A distributed implementation, especially with asynchronous messages, poses more problems. This paper discusses detecting idleness in actor systems with various characteristics, among them topology and message passing semantics. We present a TLA+ specification of a simple termination detection algorithm, add necessary conditions missing in the original description, and establish a correctness invariant to model-check the specification against. We further show via model-checking that the algorithm only works with synchronous messages but is robust against message reordering. We then model and discuss a simplified version of the idleness detection implementation in the Erlang backend of the ABS modeling language.

• Aminifar, Amin; Matin, Shokri; Rabbi, Fazle; Pun, Violet Ka I & Lamo, Yngve (2022). Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data. IEEE Access. 10, p. 6010–6027. doi: 10.1109/ACCESS.2022.3141709. Full text in Research Archive Show summary

  Artificial intelligence and machine learning have recently attracted considerable attention in the healthcare domain. The data used by machine learning algorithms in healthcare applications is often distributed over multiple sources, for instance, hospitals or patients’ personal devices. One main difficulty lies in analyzing such data without compromising patients’ privacy and personal data, which is a primary concern in healthcare applications. Therefore, in these applications, we are interested in running machine learning algorithms over distributed data without disclosing sensitive information about the data subjects. In this paper, we propose a distributed extremely randomized trees algorithm for learning from distributed data with privacy preservation. We present the implementation of our technique (which we refer to as k -PPD-ERT) on a cloud platform and demonstrate its performance based on medical data, including Heart Disease, Breast Cancer, and mental health datasets (Depresjon and Psykose datasets) associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) project.

• Kindermann, Christian & Skjæveland, Martin G (2022). A Survey of Syntactic Modelling Structures in Biomedical Ontologies, The Semantic Web – ISWC 2022. Springer. ISSN 9783031194320. p. 445–461. doi: 10.1007/978-3-031-19433-7_26. Full text in Research Archive Show summary

  Despite the large-scale uptake of semantic technologies in the biomedical domain, little is known about common modelling practices in published ontologies. OWL ontologies are often published only in the crude form of sets of axioms leaving the underlying design opaque. However, a principled and systematic ontology development life cycle is likely to be reflected in regularities of the ontology’s emergent syntactic structure. To develop an understanding of this emergent structure, we propose to reverse-engineer ontologies taking a syntax-directed approach for identifying and analysing regularities for axioms and sets of axioms. We survey BioPortal in terms of syntactic modelling trends and common practices for OWL axioms and class frames. Our findings suggest that biomedical ontologies only share simple syntactic structures in which OWL constructors are not deeply nested or combined in a complex manner. While such simple structures often account for large proportions of axioms in a given ontology, many ontologies also contain non-trivial amounts of more complex syntactic structures that are not common across ontologies.

• Giannakopoulos, Foivos Psarommatis & Vosniakos, George-Christopher (2022). Systematic Development of a Powder Deposition System for an Open Selective Laser Sintering Machine Using Analytic Hierarchy Process. Journal of Manufacturing and Materials Processing (JMMP). 6(1). doi: 10.3390/jmmp6010022. Full text in Research Archive
• Gu, Zhenzhen; Corcoglioniti, Francesco; Lanti, Davide; Mosca, Alessandro; Xiao, Guohui & Xiong, Jing [Show all 7 contributors for this article] (2022). A systematic overview of data federation systems. Semantic Web – Interoperability, Usability, Applicability. ISSN 1570-0844. 15(1), p. 107–165. doi: 10.3233/SW-223201. Full text in Research Archive Show summary

  Data federation addresses the problem of uniformly accessing multiple, possibly heterogeneous data sources, by mapping them into a unified schema, such as an RDF(S)/OWL ontology or a relational schema, and by supporting the execution of queries, like SPARQL or SQL queries, over that unified schema. Data explosion in volume and variety has made data federation increasingly popular in many application domains. Hence, many data federation systems have been developed in industry and academia, and it has become challenging for users to select suitable systems to achieve their objectives. In order to systematically analyze and compare these systems, we propose an evaluation framework comprising four dimensions: (i) federation capabilities, i.e., query language, data source, and federation techniques; (ii) data security, i.e., authentication, authorization, auditing, encryption, and data masking; (iii) interface, i.e., graphical interface, command line interface, and application programming interface; and (iv) development, i.e., main development language, deployment, commercial support, open source, and release. Using this framework, we thoroughly studied 51 data federation systems from the Semantic Web and Database communities. This paper shares the results of our investigation and aims to provide reference material and insights for users, developers and researchers selecting or further developing data federation systems.

• He, Yuan; Chen, Jiaoyan; Dong, Hang; Jimenez-Ruiz, Ernesto; Hadian, Ali & Horrocks, Ian (2022). Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13489, p. 575–591. doi: 10.1007/978-3-031-19433-7_33. Full text in Research Archive Show summary

  Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new Bio-ML track at OAEI 2022.

• Cameron, David B.; Waaler, Arild Torolv Søetorp; Fjøsna, Erlend; Hole, Monica & Giannakopoulos, Foivos Psarommatis (2022). A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries. Frontiers in Manufacturing Technology. 2. doi: 10.3389/fmtec.2022.945717. Full text in Research Archive Show summary

  The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

• Kamburjan, Eduard; Din, Crystal Chang; Schlatte, Rudolf; Tarifa, Silvia Lizeth Tapia & Johnsen, Einar Broch (2022). Twinning-by-Construction: Ensuring Correctness for Self-adaptive Digital Twins. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13701, p. 188–204. doi: 10.1007/978-3-031-19849-6_12. Full text in Research Archive Show summary

  Digital twin applications use digital artefacts to twin physical systems. The purpose is to continuously mirror the structure and behavior of the physical system, such that users can analyse the physical system by means of the digital twin. However, the physical system might change over time. In this case, the digital twin’s ensemble of digital artefacts needs to be reconfigured to correctly twin the physical system again. This paper considers a digital twin infrastructure combining MAPE-K feedback loops and semantic reflection to automatically ensure that the digital artefacts correctly twin the physical system; i.e., the resulting system is twinned-by-construction. We consider the monitoring of both structural and temporal correctness properties for digital twin, including the time delay required by reconfiguration, and the capture of execution traces to reflect digital threads in the digital twin framework.

• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Savkovic, Ognjen & Kostylev, Egor [Show all 7 contributors for this article] (2022). ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction. In Hasan, Mohammad Al & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 9781450392365. p. 5074–5078. doi: 10.1145/3511808.3557214. Full text in Research Archive Show summary

  Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently user-friendly. The main challenge arises from the trade-off: the domain KG schema should be knowledge-oriented, to reflect the general domain knowledge; while a KG schema should be dataoriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.

• Kamburjan, Eduard; Klungre, Vidar Norstein; Schlatte, Rudolf; Tarifa, Silvia Lizeth Tapia; Cameron, David & Johnsen, Einar Broch (2022). Digital Twin Reconfiguration Using Asset Models. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13704, p. 71–88. doi: 10.1007/978-3-031-19762-8_6. Full text in Research Archive
• Schlatte, Rudolf; Johnsen, Einar Broch; Kamburjan, Eduard & Tarifa, Silvia Lizeth Tapia (2022). The ABS simulator toolchain. Science of Computer Programming. ISSN 0167-6423. 223. doi: 10.1016/j.scico.2022.102861. Full text in Research Archive
• Cameron, David; Skogvang, Arnljot; Fekete, Mihaly; Martinsson, Henrik; Strand, Morten & Castro, Marcel [Show all 10 contributors for this article] (2022). The Digital Design Basis. Demonstrating a framework to reduce costs and improve quality in early‐phase design. Digital Chemical Engineering. 2. doi: 10.1016/j.dche.2022.100015. Full text in Research Archive Show summary

  As a joint initiative, Aker BP, Lundin-Norway, Aker Solutions, TechnipFMC, Aibel and Aize, together with Sirius (the oil and gas research centre at the University of Oslo) have developed and demonstrated a common digital model representation of the information in early-phase design bases for oil & gas field developemnts. The scope of the project was to develop a proof of concept for a Digital Design Basis that supports data-centric rather than document-based engineering. The project established a standards-based data model that holds data about both the design basis and functional requirements decided by an operator. This model that can be implemented in any relevant software tools in a concept study, to ensure that information shared between operators and EPC vendors, with their different software tools, have the same meaning and understanding. The model is based on a common digitalized language for communication along the field development supply chain.

• Petricek, Tomas; Burg, Gerrit J. J. van den; Nazábal, Alfredo; Ceritli, Taha; Jimenez-Ruiz, Ernesto & Williams:, Christopher K. I. (2022). AI Assistants: A Framework for Semi-Automated Data Wrangling. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(9), p. 9295–9306. doi: 10.1109/TKDE.2022.3222538. Full text in Research Archive Show summary

  Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.

• Zheng, Zhuoxun; Zhou, Baifan; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards a Visualisation Ontology for Data Analysis in Industrial Applications. CEUR Workshop Proceedings. 3355. Full text in Research Archive Show summary

  Machine learning (ML) approaches have proven their great potential in dealing with heterogeneous and voluminous data and thus are widespread in industry. To facilitate the presentation of the ML results and the subsequent discussion on that, visualisation is essential, as it effectively conveys the information behind the data. However, a standardisation of the knowledge and practice about visualisation is still lacking in the industry, which sometimes leads to misunderstandings in conveying information and thus making the discussions on the ML results error-prone. A visualisation ontology which models the nature and pipeline of visualization tasks are well suited to provide such standardisation. Currently there are a few studies that discuss partially the modelling of visualisation, however they are less adequate in depicting the practical procedure of visualisation tasks, which is highly demanded in the industrial applications. To this end, we present our ongoing work of development of the visualisation ontology in industrial applications at Bosch. We also discuss applications and evaluation of our ontology.

• Kamburjan, Eduard & Fiorini, Sandro Rama (2022). On the Notion of Naturalness in Formal Modeling. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. doi: 10.1007/978-3-031-08166-8_13. Full text in Research Archive
• Kamburjan, Eduard; Klungre, Vidar Norstein & Giese, Martin (2022). Never Mind the Semantic Gap: Modular, Lazy and Safe Loading of RDF Data. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13261. doi: 10.1007/978-3-031-06981-9_12. Full text in Research Archive
• Kamburjan, Eduard & Wasser, Nathan (2022). The Right Kind of Non-Determinism: Using Concurrency to Verify C Programs with Underspecified Semantics. Electronic Proceedings in Theoretical Computer Science (EPTCS). doi: 10.4204/EPTCS.365.1. Full text in Research Archive
• Abusdal, Ole Jørgen; Kamburjan, Eduard; Pun, Violet Ka I & Stolz, Volker (2022). A Notion of Equivalence for Refactorings with Abstract Execution. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13702, p. 259–280. doi: 10.1007/978-3-031-19756-7_15. Full text in Research Archive Show summary

  Relational verification through dynamic logic is a promising approach for verifying object oriented programs. Recent advances from symbolic to abstract executions have enabled reasoning about incomplete/abstract versions of such programs. This has proven fruitful in the exploration of correctness of refactorings primarily related to code blocks in Java. In this paper we explore further types of equivalent transformations and refactorings and discuss the challenges that still need to be overcome for full round-trip correctness of refactorings in object-oriented languages.

• Damiani, Ferruccio; Hähnle, Reiner; Kamburjan, Eduard; Lienhardt, Michael & Paolini, Luca (2022). Variability Modules. Journal of Systems and Software. ISSN 0164-1212. 195. doi: 10.1016/j.jss.2022.111510. Full text in Research Archive Show summary

  A Software Product Line (SPL) is a family of similar programs, called variants, generated from a common artifact base. A Multi SPL (MPL) is a set of interdependent SPLs: each variant can depend on variants from other SPLs. MPLs are challenging to model and to implement efficiently, especially when different variants of the same SPL must coexist and interoperate. We address this challenge by introducing the concept of a variability module (VM), a new language construct. A VM constitutes at the same time a module and an SPL of standard (variability-free), possibly interdependent, modules. Generating a variant of a VM triggers the generation of all variants required to satisfy its dependencies. Consequentially, a set of interdependent VMs represents an MPL that can be compiled into a set of standard modules. We illustrate the VM concept with an example from an industrial modeling scenario and formalize it in a core calculus. We define family-based analyses to check that a VM satisfies certain well-formedness conditions and whether all variants can be generated. Finally, we provide an implementation of VM for the Java-like modeling language ABS, and evaluate it with case studies.

• Katsidoniotaki, Eirini; Giannakopoulos, Foivos Psarommatis & Göteman, Malin (2022). Digital Twin for the Prediction of Extreme Loads on a Wave Energy Conversion System. Energies. 15(15). doi: 10.3390/en15155464. Full text in Research Archive
• Lin, Tengteng; Chen, Qiaosheng; Cheng, Gong; Soylu, Ahmet; Ell, Basil & Zhao, Ruoqi [Show all 10 contributors for this article] (2022). ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval. In Amigó, Enrique; Castells, Pablo; Gonzalo, Julio; Carterette, Ben; Culpepper, J. Shane & Kazai, Gabriella (Ed.), SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. Association for Computing Machinery (ACM). ISSN 9781450387323. p. 2981–2991. doi: 10.1145/3477495.3531729. Full text in Research Archive Show summary

  Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics. In Hasan, Mohammad Al & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 9781450392365. p. 5064–5068. doi: 10.1145/3511808.3557195. Full text in Research Archive Show summary

  Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion.

• Matentzoglu, Nicolas; Balhoff, James P.; Bello, Susan M.; Bizon, Chris; Brush, Matthew & Callahan, Tiffany J. [Show all 44 contributors for this article] (2022). A Simple Standard for Sharing Ontological Mappings (SSSOM). Database: The Journal of Biological Databases and Curation. 2022. doi: 10.1093/database/baac035. Full text in Research Archive Show summary

  Abstract Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

• Thapa, Ratan Bahadur & Giese, Martin (2022). Mapping Relational Database Constraints to SHACL. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. p. 214–230. doi: 10.1007/978-3-031-19433-7_13. Full text in Research Archive

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• Kamburjan, Eduard; Fraga, David Chaves; Nathan, Paco; Roover, Coen De; Corcho, Oscar & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2024). SOFLIM2KG-SEMIIM 2024 Joint Proceedings SOFLIM2KG and SEMIIM 2024. CEUR Workshop Proceedings. doi: https:/ceur-ws.org/Vol-3830/. Full text in Research Archive

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• Kamburjan, Eduard; Jimenez-Ruiz, Ernesto; Zhou, Baifan & Waaler, Arild Torolv Søetorp (2024). Preface: Semantic Industrial Information Modelling. CEUR Workshop Proceedings. 3830. Full text in Research Archive
• Qu, Yuanwei & Cordeiro, Fábio Corrêa (2023). Term Frequency Analysis for Semantic Modeling of Geological Fault Knowledge in the Energy Industry. Full text in Research Archive
• Holter, Ole Magnus & Ell, Basil (2023). Reading Between the Lines: Information Extraction from Industry Requirements. Full text in Research Archive Show summary

  Industry requirements describe the qualities that a project or a service must provide. Most requirements are, however, only available in natural language format and are embedded in textual documents. To be machine-understandable, a requirement needs to be represented in a logical format. We consider that a requirement consists of a scope, which is the requirement's subject matter, a condition, which is any condition that must be fulfilled for the requirement to be relevant, and a demand, which is what is required. We introduce a novel task, the identification of the semantic components scope, condition, and demand in a requirement sentence, and establish baselines using sequence labelling and few-shot learning. One major challenge with this task is the implicit nature of the scope, often not stated in the sentence. By including document context information, we improved the average performance for scope detection. Our study provides insights into the difficulty of machine understanding of industry requirements and suggests strategies for addressing this challenge.

• Elahi, Mohammad Fazleh; Ell, Basil & Cimiano, Philipp (2023). LexExMachinaQA: A framework for the automatic induction ofontology lexica for Question Answering over Linked Data. Full text in Research Archive Show summary

  An open issue for Semantic Question Answering Systems is bridging the so called lexical gap, referring to the fact that the vocabulary used by users in framing a question needs to be interpreted with respect to the logical vocabulary used in the data model of a given knowledge base or knowledge graph. Building on previous work to automatically induce ontology lexica from language corpora by using association rules to identify correspondences between lexical elements on the one hand and ontological vocabulary elements on the other, in this paper we propose LexExMachinaQA, a framework allowing us to evaluate the impact of automatically induced lexicalizations in terms of alleviating the lexical gap in QA systems. Our framework combines the LexExMachina approach for lexicon induction with the QueGG system proposed by Benz et al. that relies on grammars automatically generated from ontology lexica to parse questions into SPARQL. We show that automatically induced lexica yield a decent performance i.t.o. F1 measure with respect to the QLAD-7 dataset, representing a 34% - 56% performance degradation with respect to a manually created lexicon. While these results show that the fully automatic creation of lexica for QA systems is not yet feasible, the method could certainly be used to bootstrap the creation of a lexicon in a semi-automatic manner, thus having the potential to significantly reduce the human effort involved.

• Qu, Yuanwei; Zheng, Zhuoxun; Zhou, Baifan; Zhou, Yan; Santos, Nicolau & Savkovic, Ognjen [Show all 8 contributors for this article] (2023). Towards Data Integrity Verification for More Sustainable Petroleum Industry. Full text in Research Archive
• He, Yuan; Chen, Jiaoyan; Jimenez-Ruiz, Ernesto; Dong, Hang & Horrocks, Ian (2023). Language Model Analysis for Ontology Subsumption Inference. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2302.06761. Full text in Research Archive
• Aviso, Kathleen; Zhang, Dongda; Cameron, David B. & Xuan, Jin (2022). Editorial: Machine learning for chemical processes. Digital Chemical Engineering. 5. doi: 10.1016/j.dche.2022.100062. Full text in Research Archive
• Waaler, Arild Torolv Søetorp; Kharlamov, Evgeny; Zhou, Baifan & Zhou, Dongzhuoran (2022). Proceedings of the First International Workshop on Semantic Industrial Information Modelling (SemIIM 2022) co-located with the 19th Extended Semantic Web Conference ESWC 2022, Greece, Crete, 30 May 2022. Full text in Research Archive
• Zhou, Baifan; Tan, Zhipeng; Zheng, Zhuoxun; Zhou, Dongzhuoran; He, Yunjie & Zhu, Yuqicheng [Show all 11 contributors for this article] (2022). Neuro-Symbolic AI at Bosch: Data Foundation, Insights, and Deployment. Full text in Research Archive
• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Tan, Zhipeng; Kostylev, Egor & Kharlamov, Evgeny (2022). Towards Executable Knowledge Graph Translation. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Khan, Akif Quddus; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards A Statistic Ontology for Data Analysis in Smart Manufacturing. Full text in Research Archive
• Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Tan, Zhipeng; Savkovic, Ognjen & Yang, Hui [Show all 8 contributors for this article] (2022). Knowledge Graph-Based Semantic System for Visual Analytics in Automatic Manufacturing. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch. In Hasan, Mohammad Al & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 9781450392365. p. 5102–5103. doi: 10.1145/3511808.3557512. Full text in Research Archive Show summary

  With the development of Industry 4.0 technology, modern industries such as Bosch’s welding monitoring witnessed the rapid widespread of machine learning (ML) based data analytical applications, which in the case of welding monitoring has led to more efficient and accurate welding monitoring quality. However, industrial ML is affected by the low transparency of ML towards non-ML experts needs. The lack of understanding by domain experts of ML methods hampers the application of ML methods in industry and the reuse of developed ML pipelines, as ML methods are often developed in an ad hoc manner for specific problems. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG), which formally encode ML knowledge and solutions in KGs, which serve as common language between ML experts and nonML experts, thus facilitate their communication and increase the transparency of ML methods. We evaluated our system extensively with an industrial use case at Bosch, showing promising results.

• Garcez, Artur d’Avila & Jimenez-Ruiz, Ernesto (2022). Preface. CEUR Workshop Proceedings. 3212. Full text in Research Archive
• Qu, Yuanwei (2022). Geological Information Capture with Sketches and Ontologies. Full text in Research Archive
• Johnsen, Einar Broch (2022). Digital Twins as Evolving Model-Centric Systems. Full text in Research Archive
• Johnsen, Einar Broch (2022). Digital Twins: An Emerging Paradigm for Model-Centric Engineering. Full text in Research Archive
• Johnsen, Einar Broch (2022). Digital Twins: From Engineering to Science. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Dongzhuoran, Zhou; Ahmet, Soylu & Evgeny, Kharlamov (2022). ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics. Full text in Research Archive
• Qu, Yuanwei; Zhou, Baifan; Kharlamov, Evgeny & Giese, Martin (2022). Industrial Geological Information Capture with GeoStructure Ontology. Full text in Research Archive Show summary

  Understanding geological structures plays an essential role in a wide range of industries. Various tools and software have been developed to support the observation and characterisation of the geological structures and they produce a large volume of heterogeneous data. Thanks to the development of semantic technologies, plenty of works have investigated industrial geological data. In contrast, geological sketches, figure interpretations, and illustrations as the initial data collection and analysis steps have received little attention. Most structure information is still reported as PDF files with forms, tables, and raster images with interpretations. We attempt to fill this gap by providing an ontology- based industrial geological information capture method to allow users to input and store structure information formally. Following users’ sketches, the corresponding knowledge graphs will be generated. This work aims to make the first-hand geological structure information findable, accessible, interoperable, reusable (FAIR), and to support qualitative information reasoning, from a user-driven perspective, and to meet the needs of digitised industry.

• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Cheng, Gong & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2022). Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case. Full text in Research Archive
• Dongzhuoran, Zhou; Zhou, Baifan; Zhuoxun, Zheng; Ahmet, Soylu; Ognjen, Savkovic & V., Kostylev Egor [Show all 7 contributors for this article] (2022). ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction. Full text in Research Archive
• Skjæveland, Martin G; Slaughter, Laura Ann & Kindermann, Christian (2022). OntoCommons D4.3 - Report on Landscape Analysis of Ontology Engineering Tools EU project. EU ONTO Commons Grant Agreement no. 958371. Full text in Research Archive
• Skjæveland, Martin G & Kindermann, Christian (2022). Concrete Names for Complex Expressions in Ontologies: A Case Study on SNOMED CT. CEUR-WS-org.Vol.3257- Paper 12. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Soylu, Ahmet [Show all 7 contributors for this article] (2022). Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring. Full text in Research Archive
• Thapa, Ratan Bahadur & Giese, Martin (2023). Optimizing SPARQL Queries with SHACL (Extended Version). Universitetet i Oslo. ISSN 9788273686046. Full text in Research Archive Show summary

  We propose a set of optimizations that can be applied to a given SPARQL query, and that guarantee that the optimized query has the same answers under bag semantics as the original query, provided that the queried RDF graph validates certain SHACL constraints. Our optimizations exploit the relationship between graph patterns in the SPARQL queries and the SHACL constraints that describe those patterns in the RDF graph. We prove the correctness of these optimizations and show how they can be propagated to larger queries while preserving answers. Further, we prove the confluence of rewritings that employ these optimizations, guaranteeing convergence to the same optimized query regardless of the rewriting order.

• Kamburjan, Eduard; Hallerstede, Stefan & Kamburjan, Eduard (2023). Preproceedings of the Workshop on Applications of Formal Methods and Digital Twins. Universitetet i Oslo. ISSN 9788273686053. Full text in Research Archive Show summary

  This technical reports contains the papers of the Workshop on Ap- plications of Formal Methods and Digital Twins, which is held 06.03.23 in Lübeck, Germany, co-located with the 25th International Symposium on Formal Methods. All papers were peer-reviewed prior to inclusion in this volume by the program committee of the workshop, and we thank the member of the PC for their constructive reviews. Additionally to the papers, the workshop will feature an invited talk by Ana Cavalcanti title "RoboStar Twins?". - Leveraging Runtime Verification for the Monitoring of Digital Twins by Sylvain Hallé, Chukri Soueidi and Yliès Falcone - Emerging Challenges in Compositionality and Correctness for Digital Twins by Eduard Kamburjan, Vidar Klungre, Silvia Lizeth Tapia Tarifa, Rudolf Schlatte, Martin Giese, David Cameron and Einar Broch Johnsen - Are Formal Contracts a useful Digital Twin of Software Systems? by Jonas Schiff and Alexander Weigl - Digital Twin for Rescue Missions - a Case Study by Martin Leucker, Martin Sachenbacher and Lars Bernd Vosteen - A Digital Twin for Coupling Mobility and Energy Optimization: The ReNuBiL Living Lab by Daniel Thoma, Martin Sachenbacher, Martin Leucker and Aliyu Tanko Ali - Mining Digital Twins of a VPN Server by Andrea Pferscher, BenjaminWunderling, Bernhard K. Aichernig and Edi Muskardin

• He, Yuan; Chen, Jiaoyan; Dong, Hang; Jimenez-Ruiz, Ernesto; Hadian, Ali & Horrocks, Ian (2022). Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. arXivLabs. Full text in Research Archive
• Chen, Jiaoyan; He, Yuan; Geng, Yuxia; Jimenez-Ruiz, Ernesto; Dong, Hang & Horrocks, Ian (2022). Contextual Semantic Embeddings for Ontology Subsumption Prediction. arXivLabs. Full text in Research Archive
• Blum, Moritz; Ell, Basil & Cimiano, Philipp (2022). Exploring the impact of literal transformations within Knowledge Graphs for Link Prediction. ACM Publications. Full text in Research Archive
• Kamburjan, Eduard; Klungre, Vidar Norstein & Giese, Martin (2022). Never Mind the Semantic Gap: Modular, Lazy and Safe Loading of RDF Data (Full Paper). Universitetet i Oslo. ISSN 9788273686022. Full text in Research Archive Show summary

  Any attempt at a tight integration between semantic technologies and object oriented programming will invariably stumble over the gap between the two underlying object models. We illustrate how this semantic gap manifests from the point of view of data retrieval with SPARQL. We present a novel mechanism to load data from RDF knowledge graphs into object-oriented languages that gives static guarantees about the data access and modularly integrates the mapping between the program and the RDF view with the class definition in the program. This allows us to preserve the separation of concerns between the class system of RDF (geared towards domain modeling and data), and that of the program (geared towards typing and code reuse). Loading of RDF can be performed lazily, when required by the program, based on query-futures - subqueries that are only evaluated if and when the data is accessed. We formulate a Liskov principle for the mapping queries to characterize when they respect the subclass relation. Moreover, we provide tool support to detect when the user-provided mapping would cause the loading mechanisms to result in data structures that manifest the semantic gap.

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