Latest publications – SIRIUS

• 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.
• 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
• Qu, Yuanwei; Kamburjan, Eduard; Torabi, Anita & Giese, Martin (2024). Semantically triggered qualitative simulation of a geological process. Applied Computing and Geosciences. ISSN 2590-1974. 21. doi: 10.1016/j.acags.2023.100152.
• 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. ISSN 1613-0073. 3632. Full text in Research Archive
• Zheng, Zhuoxun; Savkovic, Ognjen; Nikolov, Nikolay; Phuc Luu, Huu; 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. ISSN 1613-0073. 3632. Full text in Research Archive
• 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. ISSN 1613-0073. 3632. 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
• 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.

• Psarommatis Giannakopoulos, Foivos; 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 978-3-031-38165-2. p. 451–459. doi: 10.1007/978-3-031-38165-2_53.
• Fraile, Francisco; Psarommatis Giannakopoulos, Foivos; Alarcón, Faustino & Joan, Jordi (2023). A Methodological Framework for Designing Personalised Training Programs to Support Personnel Upskilling in Industry 5.0. Computers. ISSN 2073-431X. 12(11). doi: 10.3390/computers12110224.
• 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 978-981-99-7024-7. p. 466–473. doi: 10.1007/978-981-99-7025-4_41. Full text in Research Archive
• 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 978-3-031-33455-9.
• 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
• Psarommatis Giannakopoulos, Foivos & May, Gökan (2023). A Systematic Analysis for Mapping Product-Oriented and Process-Oriented Zero-Defect Manufacturing (ZDM) in the Industry 4.0 Era. Sustainability. ISSN 2071-1050. 15(16). doi: 10.3390/su151612251.
• 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. 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.

• 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.
• 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.
• 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. 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.

• 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.
• 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. ISSN 2169-3536. 11, p. 37360–37377. doi: 10.1109/ACCESS.2023.3267000. Full text in Research Archive
• Psarommatis Giannakopoulos, Foivos; 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.
• Psarommatis Giannakopoulos, Foivos; 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
• May, Gokan & Psarommatis Giannakopoulos, Foivos (2023). Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research. Energies. ISSN 1996-1073. 16(10). doi: 10.3390/en16104179.
• 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. ISSN 0027-8424. 120(10). doi: 10.1073/pnas.2220080120. Full text in Research Archive
• Azamfirei, Victor; Psarommatis Giannakopoulos, Foivos & 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
• 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
• 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.
• 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.
• Zheng, Zhuoxun; Zhou, Baifan; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards a Visualisation Ontology for Data Analysis in Industrial Applications. CEUR Workshop Proceedings. ISSN 1613-0073. 3355. Full text in Research Archive
• Kindermann, Christian & Skjæveland, Martin G (2022). A Survey of Syntactic Modelling Structures in Biomedical Ontologies, The Semantic Web – ISWC 2022. Springer. ISSN 978-3-031-19432-0. p. 445–461. doi: 10.1007/978-3-031-19433-7_26.
• Balog, Krisztian; Mirza, Paramita; Skjæveland, Martin G & Wang, Zhilin (2022). Report on the Workshop on Personal Knowledge Graphs (PKG 2021) at AKBC 2021. SIGIR Forum. ISSN 0163-5840. 56(1). Full text in Research Archive
• 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 Al Hasan, Mohammad & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 978-1-4503-9236-5. p. 5074–5078. doi: 10.1145/3511808.3557214. 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.

• 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. 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.

• 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 Journal. ISSN 1570-0844. 15(1), p. 107–165. doi: 10.3233/SW-223201. Full text in Research Archive
• 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
• Petricek, Tomas; van den Burg, Gerrit J. J.; 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.

• 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.
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics. In Al Hasan, Mohammad & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 978-1-4503-9236-5. p. 5064–5068. doi: 10.1145/3511808.3557195.
• 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 978-1-4503-8732-3. p. 2981–2991. doi: 10.1145/3477495.3531729. 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.

• Psarommatis Giannakopoulos, Foivos & May, Gokan (2022). Achieving Global Sustainability Through Sustainable Product Life Cycle. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. doi: 10.1007/978-3-031-16407-1_46.
• Psarommatis Giannakopoulos, Foivos & May, Gokan (2022). A standardized approach for measuring the performance and flexibility of digital twins. International Journal of Production Research. ISSN 0020-7543. p. 1–16. doi: 10.1080/00207543.2022.2139005.
• Psarommatis Giannakopoulos, Foivos & Bravos, George (2022). A holistic approach for achieving Sustainable manufacturing using Zero Defect Manufacturing: a conceptual Framework. Procedia CIRP. ISSN 2212-8271. 107, p. 107–112. doi: 10.1016/j.procir.2022.04.018.
• Katsidoniotaki, Eirini; Psarommatis Giannakopoulos, Foivos & Göteman, Malin (2022). Digital Twin for the Prediction of Extreme Loads on a Wave Energy Conversion System. Energies. ISSN 1996-1073. 15(15). doi: 10.3390/en15155464.
• 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). ISSN 2075-2180. doi: 10.4204/EPTCS.365.1.
• 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.
• Kamburjan, Eduard & Rama Fiorini, Sandro (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.
• 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
• Schlatte, Rudolf; Johnsen, Einar Broch; Kamburjan, Eduard & Tapia Tarifa, Silvia Lizeth (2022). The ABS simulator toolchain. Science of Computer Programming. ISSN 0167-6423. 223. doi: 10.1016/j.scico.2022.102861.
• Kamburjan, Eduard; Din, Crystal Chang; Schlatte, Rudolf; Tapia Tarifa, Silvia Lizeth & 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
• Kamburjan, Eduard; Klungre, Vidar Norstein; Schlatte, Rudolf; Tapia Tarifa, Silvia Lizeth; 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.
• Kamburjan, Eduard & Johnsen, Einar Broch (2022). Knowledge Structures Over Simulation Units. In Ruiz Martin, Martin; Emami, Niloufar; Blas, María Julia & Rezaee, Roya (Ed.), Annual Modeling and Simulation Conference, ANNSIM 2022. IEEE Press. ISSN 978-1-71-385288-9. p. 78–89. doi: 10.23919/ANNSIM55834.2022.9859490.
• Cameron, David B.; Waaler, Arild Torolv Søetorp; Fjøsna, Erlend; Hole, Monica & Psarommatis Giannakopoulos, Foivos (2022). A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries. Frontiers in Manufacturing Technology. ISSN 2813-0359. 2. doi: 10.3389/fmtec.2022.945717. Full text in Research Archive
• Ghadrdan, Maryam & Cameron, David B. (2022). An Overview of the Evolution of Oil and Gas 4.0. In Sharifzadeh, Mahdi (Eds.), Industry 4.0 Vision for Energy and Materials: Enabling Technologies and Case Studies. John Wiley & Sons. ISSN 9781119695936. p. 241–268. doi: 10.1002/9781119695868.ch9. Show summary

  The world's largest economies are already acting on strategies toward Industry 4.0 (4IR). Germany is today's leading in the manufacturing and process industry with the Platform Industry 4.0, gathering government, scientific community, industry, and trade unions working toward a common vision. Global collaboration and alliances ensure a common direction toward standardization. This chapter provides a brief overview of application of Industry 4.0 methods and standards in the oil and gas industry, with examples from the energy company Equinor.

• Psarommatis Giannakopoulos, Foivos & May, Gokan (2022). A literature review and design methodology for digital twins in the era of zero defect manufacturing. International Journal of Production Research. ISSN 0020-7543. doi: 10.1080/00207543.2022.2101960.
• 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. ISSN 2041-1480. 13(1). doi: 10.1186/s13326-022-00270-8.
• 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. ISSN 1758-0463. 2022. doi: 10.1093/database/baac035. 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). Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 123–128. doi: 10.1007/978-3-031-11609-4_23. Full text in Research Archive
• Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Trung-Kien, Tran [Show all 11 contributors for this article] (2022). The Data Value Quest: A Holistic Semantic Approach at Bosch. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 287–290. doi: 10.1007/978-3-031-11609-4_42. Full text in Research Archive
• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Kostylev, Egor; Cheng, Gong & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2022). Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 299–302. doi: 10.1007/978-3-031-11609-4_45. Full text in Research Archive
• Dreyfus, Paul-Arthur; Pélissier, Antoine; Psarommatis Giannakopoulos, Foivos & Kiritsis, Dimitris (2022). Data-based model maintenance in the era of industry 4.0: A methodology. Journal of manufacturing systems. ISSN 0278-6125. 63, p. 304–316. doi: 10.1016/j.jmsy.2022.03.015. Full text in Research Archive
• Psarommatis Giannakopoulos, Foivos & 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). ISSN 2504-4494. 6(1). doi: 10.3390/jmmp6010022.
• 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 978-3-030-99618-5. p. 567–577. doi: 10.1007/978-3-030-99619-2_53. 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.

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• Tapia Tarifa, Silvia Lizeth & Proença, José (2022). Formal Aspects of Component Software. 18th International Conference, FACS 2022, Virtual Event, November 10–11, 2022, Proceedings. Springer. ISBN 978-3-031-20871-3. 13712(13712). 245 p.

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• 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.
• Qu, Yuanwei & Cordeiro, Fábio Corrêa (2023). Term Frequency Analysis for Semantic Modeling of Geological Fault Knowledge in the Energy Industry.
• 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.
• 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.
• 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.
• Elahi, Mohammad Fazleh; Ell, Basil & Cimiano, Philipp (2023). LexExMachinaQA: A framework for the automatic induction ofontology lexica for Question Answering over Linked Data. 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.

• Holter, Ole Magnus & Ell, Basil (2023). Reading Between the Lines: Information Extraction from Industry Requirements. 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.

• 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.
• 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.
• Aviso, Kathleen; Zhang, Dongda; Cameron, David B. & Xuan, Jin (2022). Editorial: Machine learning for chemical processes. Digital Chemical Engineering. ISSN 2772-5081. 5. doi: 10.1016/j.dche.2022.100062.
• 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. .
• 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.
• Horrocks, Ian; Olivares, Jordi; Cocchi, Valerio; Motik, Boris & Roy, Dylan (2022). The Dow Jones Knowledge Graph. Show summary

  Dow Jones is a leading provider of market, industry and portfolio intelligence serving a wide range of financial applications including asset management, trading, analysis and bankruptcy/restructuring. The information needed to provide such intelligence comes from a variety of heterogeneous data sources. Integrating this information and answering complex queries over it presents both conceptual and computational challenges. In order to address these challenges Dow Jones have used the RDFox system to integrate the various sources in a large RDF knowledge graph. The knowledge graph is being used to power an expanding range of internal processes and market intelligence products.

• Horrocks, Ian; He, Yuan; Chen, Jiaoyan; Jimenez-Ruiz, Ernesto & Hadian, Ali (2022). Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. 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.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch. In Al Hasan, Mohammad & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 978-1-4503-9236-5. p. 5102–5103. doi: 10.1145/3511808.3557512. 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.

• Qu, Yuanwei (2022). Geological Information Capture with Sketches and Ontologies.
• 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.
• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Tan, Zhipeng; Kostylev, Egor & Kharlamov, Evgeny (2022). Towards Executable Knowledge Graph Translation.
• 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.
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Khan, Akif Quddus; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards A Statistic Ontology for Data Analysis in Smart Manufacturing.
• Yahya, Muhammad; Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Breslin, John G. & Ali, Muhammad Intizar [Show all 7 contributors for this article] (2022). Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction.
• 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.
• 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.
• Dongzhuoran, Zhou; Zhou, Baifan; Zhuoxun, Zheng; Ahmet, Soylu; Ognjen, Savkovic & Egor V., Kostylev [Show all 7 contributors for this article] (2022). ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction.
• Zheng, Zhuoxun; Zhou, Baifan; Dongzhuoran, Zhou; Ahmet, Soylu & Evgeny, Kharlamov (2022). ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics.
• Johnsen, Einar Broch (2022). Digital Twins: An Emerging Paradigm for Model-Centric Engineering.
• Johnsen, Einar Broch (2022). Digital Twins as Evolving Model-Centric Systems.
• Johnsen, Einar Broch (2022). Digital Twins: From Engineering to Science.
• d’Avila Garcez, Artur & Jimenez-Ruiz, Ernesto (2022). Preface. CEUR Workshop Proceedings. ISSN 1613-0073. 3212.
• Qu, Yuanwei; Zhou, Baifan; Kharlamov, Evgeny & Giese, Martin (2022). Industrial Geological Information Capture with GeoStructure Ontology. 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.

• Thapa, Ratan Bahadur & Giese, Martin (2023). Optimizing SPARQL Queries with SHACL (Extended Version). Universitetet i Oslo. Institutt for informatikk Norway. ISSN 978-82-7368-604-6. 0806-3036(504). 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 (2023). Preproceedings of the Workshop on Applications of Formal Methods and Digital Twins. Universitetet i Oslo. ISSN 978-82-7368-605-3. 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.
• 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.
• Blum, Moritz; Ell, Basil & Cimiano, Philipp (2022). Exploring the impact of literal transformations within Knowledge Graphs for Link Prediction. ACM Publications.
• Thapa, Ratan Bahadur & Giese, Martin (2022). Mapping Relational Database Constraints to SHACL (Extended Version). Universitetet i Oslo. Institutt for informatikk. ISSN 978-82-7368-603-9. 0806-3036(503). Full text in Research Archive Show summary

  Most structured data today is still stored in relational databases, which makes it important to provide a translation between relational and semantic data. A relational to RDF mapping, such as R2RML [14], provides a way to view existing relational data in the RDF data model through declarative mappings. While relational to RDF mapping translates relational instance data to RDF, it does not specify any translation of existing relational constraints such as primary and foreign key constraints. Since the introduction of R2RML, interest in RDF constraint languages has increased and SHACL [17] has been standardised. This raises the question of which SHACL constraints are guaranteed to be valid on a dataset produced by a relational to RDF mapping. For arbitrary SQL constraints and relational to RDF mappings, this is a hard problem, but we introduce a number of restrictions on the mappings that allow us to introduce a constraint rewriting for relational to RDF mappings that faithfully transfers SQL integrity constraints to SHACL constraints. We define and prove two fundamental properties, namely maximal semantics preservation and monotonicity.

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