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Barn, B. S., Barat, S. & Sandkuhl, K. (2024). Adaptation of enterprise modeling methods for large language models. In: The practice of enterprise modeling: 16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023, Proceedings. Paper presented at 16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023 (pp. 3-18). Cham: Springer
Open this publication in new window or tab >>Adaptation of enterprise modeling methods for large language models
2024 (English)In: The practice of enterprise modeling: 16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023, Proceedings, Cham: Springer, 2024, p. 3-18Conference paper, Published paper (Refereed)
Abstract [en]

Large language models (LLM) are considered by many researchers as promising technology for automating routine tasks. Results from applying LLM in engineering disciplines such as Enterprise Modeling also indicate potential for the support of modeling activities. LLMs are fine-tuned for specific tasks using chat based interaction through the use of prompts. This paper aims at a detailed investigation of the potential of LLMs in Enterprise Modeling (EM) by taking the perspective of EM method adaptation of selected parts of the modeling process within the context of using prompts to interrogate the LLM. The research question addressed is: What adaptations in EM methods have to be made to exploit the potential of prompt based interaction with LLMs? The main contributions are (1) a meta-model for prompt engineering that integrates the concepts of the modeling domain under consideration with the notation of the modeling language applied and the input and output of prompts, (2) an investigation into the general potential of LLM in EM methods and its application in the 4EM method, and (3) implications for enterprise modeling methods.

Place, publisher, year, edition, pages
Cham: Springer, 2024
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 497
Keywords
ChatGPT, Enterprise Modeling, Large Language Model, Modeling Method, Prompt meta-model, Computational linguistics, Engineering disciplines, Enterprise models, Language model, Meta model, Metamodeling, Model method, Specific tasks, Modeling languages
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-63353 (URN)10.1007/978-3-031-48583-1_1 (DOI)2-s2.0-85178574971 (Scopus ID)9783031485824 (ISBN)9783031485831 (ISBN)
Conference
16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved
Griesch, L., Rittelmeyer, J. & Sandkuhl, K. (2024). Towards AI as a Service for Small and Medium-Sized Enterprises (SME). In: J. P. A. Almeida, M. Kaczmarek-Heß, A. Koschmider, H. A. Proper (Ed.), The Practice of Enterprise Modeling: 16th IFIPWG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2023. Paper presented at 16th IFIPWG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2023 Vienna 28 November 2023 through 1 December 2023 (pp. 37-53). Springer, 497
Open this publication in new window or tab >>Towards AI as a Service for Small and Medium-Sized Enterprises (SME)
2024 (English)In: The Practice of Enterprise Modeling: 16th IFIPWG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2023 / [ed] J. P. A. Almeida, M. Kaczmarek-Heß, A. Koschmider, H. A. Proper, Springer, 2024, Vol. 497, p. 37-53Conference paper, Published paper (Refereed)
Abstract [en]

AI-as-a-Service (AIaaS) combines Artificial Intelligence (AI) and cloud computing to make AI accessible to enterprises without implementing complex solutions or technologies on-premise. Many small and medium-sized enterprises (SME) that lack competencies in the AI and technology sector consider AIaaS as a promising option to implement AI solutions. However, the differences between AIaaS and AI on-premise have not attracted much research. The intention of this paper is to contribute to this area by analysing the literature in the field and investigating a concrete example in more detail. Exploring AIaaS is crucial to better understand the opportunities and limitations of AI services. The contributions of the paper are (a) an analysis of the literature on AIaaS to identify factors affecting AI implementation and how AIaaS solutions differ from on-premise solutions when introducing AI in a company, (b) a case study of an SME that compares AIaaS and AI on-premise in practice, and (c) the application potential of a morphological box to compare AIaaS and AI on-premise.

Place, publisher, year, edition, pages
Springer, 2024
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 497
Keywords
AI, AI introduction, AI-as-a-Service, SME, Artificial intelligence introduction, Artificial intelligence-as-a-service, Case-studies, Cloud-computing, Complex solution, Intelligence services, Small and medium-sized enterprise, Technology sectors, Artificial intelligence
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:hj:diva-63080 (URN)10.1007/978-3-031-48583-1_3 (DOI)2-s2.0-85178563859 (Scopus ID)978-3-031-48582-4 (ISBN)978-3-031-48583-1 (ISBN)
Conference
16th IFIPWG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2023 Vienna 28 November 2023 through 1 December 2023
Available from: 2023-12-19 Created: 2023-12-19 Last updated: 2023-12-19Bibliographically approved
Reiz, A. & Sandkuhl, K. (2023). A critical view on the OQuaRE Ontology Quality Framework. In: Enterprise Information Systems: 24th International Conference, ICEIS 2022, Virtual Event, April 25–27, 2022, Revised Selected Papers. Paper presented at 24th International Conference, ICEIS 2022, Virtual Event, April 25–27, 2022 (pp. 273-291). Cham: Springer
Open this publication in new window or tab >>A critical view on the OQuaRE Ontology Quality Framework
2023 (English)In: Enterprise Information Systems: 24th International Conference, ICEIS 2022, Virtual Event, April 25–27, 2022, Revised Selected Papers, Cham: Springer, 2023, p. 273-291Conference paper, Published paper (Refereed)
Abstract [en]

Creating and maintaining high-quality ontologies is crucial as they encapsulate the logic for rule-based artificial intelligence. One way to objectively assess ontologies is ontology metrics, and the research community has already proposed measurement frameworks for assessing ontology quality. Arguably, the most holistic framework available is OQuaRE. Not only does it suggest metrics, but it also links these metrics to quality characteristics and interprets them using a school-like grading system. However, in an effort to implement the framework for the metric calculation software NEOntometrics, serious drawbacks came to light: (1) Important parts of the framework are only available at online resources (thus not part of the scientific record and without ensured permanent availability). (2) The proposing authors have been involved in all applications of the framework and corresponding research, an outside perspective is missing. (3) At times, the meaning of metrics changed throughout the papers without notice, leading to conflicting definitions. (4) A thorough evaluation of the quality grades is missing. This research aims to fill this gap: We first scrutinize the various metric proposals with their conflicting versions and resolve these conflicts. Afterward, we present OQuaRE to its full extent to create a permanent, lasting record. At last, we use a large body of evaluated ontologies to assess whether OQuaREs quality ratings sufficiently cover the ontology models used in the wild.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 487
Keywords
NEOntometrics, Ontology Quality, OQuaRE, Quality Framework, Grading, Quality control, High quality, Holistic frameworks, Neontometric, Ontology metrics, Ontology qualities, Ontology's, Research communities, Rule based, Ontology
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-63369 (URN)10.1007/978-3-031-39386-0_13 (DOI)2-s2.0-85172733445 (Scopus ID)9783031393853 (ISBN)9783031393860 (ISBN)
Conference
24th International Conference, ICEIS 2022, Virtual Event, April 25–27, 2022
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved
Hellmanzik, B. & Sandkuhl, K. (2023). A data value matrix: Linking FAIR data with business models. In: S. Nurcan, A. L. Opdahl, H. Mouratidis & A. Tsohou (Ed.), Research Challenges in Information Science: Information Science and the Connected World17th International Conference, RCIS 2023, Corfu, Greece, May 23–26, 2023, Proceedings. Paper presented at 17th International Conference, RCIS 2023, Corfu, Greece, May 23–26, 2023 (pp. 585-592). Cham: Springer
Open this publication in new window or tab >>A data value matrix: Linking FAIR data with business models
2023 (English)In: Research Challenges in Information Science: Information Science and the Connected World17th International Conference, RCIS 2023, Corfu, Greece, May 23–26, 2023, Proceedings / [ed] S. Nurcan, A. L. Opdahl, H. Mouratidis & A. Tsohou, Cham: Springer, 2023, p. 585-592Conference paper, Published paper (Refereed)
Abstract [en]

Data is the raw material of digitization, but its economic use and potential economic benefits are not always clear. Therefore, we would like to show that the processing of data, especially according to FAIR principles, plays an enormous role in enabling business models and improving existing business models. This is being tested within the EU funded project Marispace-X, part of the Gaia-X initiative. The maritime domain in particular currently still suffers from a lack of digitization: while as much data is being collected as ever before, this data is often kept in silos and hardly reused or even shared. This work therefore involved linking the FAIR principles to a data value chain, which together with business model dimensions form a data value matrix. This application of this matrix was carried out together with practice partners using the example of maritime data processing in the use case “offshore wind” and can be used and adapted as an analysis tool for data-driven business models.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 476
Keywords
business model, Data value chain, dataspace, FAIR, Data handling, Economic and social effects, Matrix algebra, Business models, Data space, Data values, Digitisation, Economic potentials, Economic use, Value chains, Value matrixes, Offshore oil well production
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-63357 (URN)10.1007/978-3-031-33080-3_41 (DOI)2-s2.0-85163385364 (Scopus ID)9783031330797 (ISBN)9783031330803 (ISBN)
Conference
17th International Conference, RCIS 2023, Corfu, Greece, May 23–26, 2023
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved
Nast, B., Reiz, A., Ivanovic, N. & Sandkuhl, K. (2023). A modeling approach supporting digital twin engineering: Optimizing the energy consumption of air conditioning facilities. In: 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C): Conference Proceedings. Paper presented at 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Västerås, Sweden, October 1-6, 2023 (pp. 479-483). IEEE Computer Society
Open this publication in new window or tab >>A modeling approach supporting digital twin engineering: Optimizing the energy consumption of air conditioning facilities
2023 (English)In: 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C): Conference Proceedings, IEEE Computer Society, 2023, p. 479-483Conference paper, Published paper (Refereed)
Abstract [en]

Digital Twin (DT) is a concept that has become increasingly relevant in recent years. The basic idea is to connect (physical) facilities or devices with digital representations to monitor, manipulate and predict their behavior in real-time. This work presents a modeling approach supporting DT engineering for air conditioning facilities. We describe the architecture of our system for developing digital representations of physically existing facilities in an industrial use case. The paper focuses on (a) the developed modeling tool, (b) the cloud service configuration, and (c) data analysis that enables energy optimizations.

Place, publisher, year, edition, pages
IEEE Computer Society, 2023
Keywords
Air Conditioning, Atmospheric Modeling, Digital Representation, Reliability Engineering, Data Models, Real Time Systems, Behavioral Sciences, Digital Twin, Model Driven Development, Mod Eling Methodologies, Domain Specific Modeling Language, Object Object Object Object, Object Object, Cloud Computing Object Object Object Object Object Object, Time Series Data, Physical Body Object Object Object Object Object Object, Physical System, Notebook, Domain Experts, Object Object Object Object Object Object, Physical Devices, Need For Solutions, Heat Recovery, Metadata File, Predictive Maintenance, Airflow Direction
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-63354 (URN)10.1109/MODELS-C59198.2023.00083 (DOI)979-8-3503-2498-3 (ISBN)
Conference
2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Västerås, Sweden, October 1-6, 2023
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved
Rittelmeyer, J. D. & Sandkuhl, K. (2023). A Morphological Box for AI Solutions: Evaluation, Refinement and Application Potentials. In: M. Ruiz, P. Soffer (Ed.), Advanced Information Systems Engineering Workshops: Proceedings. Paper presented at CAiSE 2023 International Workshops, Zaragoza, Spain, June 12–16, 2023 (pp. 5-16). Springer
Open this publication in new window or tab >>A Morphological Box for AI Solutions: Evaluation, Refinement and Application Potentials
2023 (English)In: Advanced Information Systems Engineering Workshops: Proceedings / [ed] M. Ruiz, P. Soffer, Springer, 2023, p. 5-16Conference paper, Published paper (Refereed)
Abstract [en]

The understanding of the concepts of AI is still one of the major problems for companies that want to implement AI solutions, from our own experience. Because of that, we presented an initial version of a morphological box that supports the understanding of AI solutions in previous work [1]. We now evaluated the morphological box with expert interviews from different domains and a short literature search. We present the planning, conduction and analysis of the interviews as well as a refined version of the morphological box for AI solutions. For the analysis we used the approach of qualitative content analysis by Mayring & Fenzl [2]. The interviews helped to identify several new features and values and they offered interesting insights into important aspects for AI introduction and into the introduction process itself from practitioners’ perspectives. Furthermore, we illustrate some of the boxes application potential along the information system development process and future improvement and application potentials.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 482
Keywords
AI context, artificial intelligence, Morphological box, organizational AI solutions, Content analysis, Development process improvements, Different domains, Future improvements, Information system development process, Literature search, Organisational, Organizational AI solution
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:hj:diva-62187 (URN)10.1007/978-3-031-34985-0_1 (DOI)2-s2.0-85164261009 (Scopus ID)978-3-031-34984-3 (ISBN)978-3-031-34985-0 (ISBN)
Conference
CAiSE 2023 International Workshops, Zaragoza, Spain, June 12–16, 2023
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-10-27Bibliographically approved
Reiz, A. & Sandkuhl, K. (2023). A proposal for an ontology metrics selection process. In: J. Filipe, M. Śmiałek, A. Brodsky & S. Hammoudi (Ed.), Proceedings of the 25th International Conference on Enterprise Information Systems - (Volume 1): . Paper presented at 25th International Conference on Enterprise Information Systems, April 24-26, 2023, Prague, Czech Republic (pp. 583-590). SciTePress, 1
Open this publication in new window or tab >>A proposal for an ontology metrics selection process
2023 (English)In: Proceedings of the 25th International Conference on Enterprise Information Systems - (Volume 1) / [ed] J. Filipe, M. Śmiałek, A. Brodsky & S. Hammoudi, SciTePress, 2023, Vol. 1, p. 583-590Conference paper, Published paper (Refereed)
Abstract [en]

Ontologies are the glue for the semantic web, knowledge graphs, and rule-based intelligence in general. They build on description logic, and their development is a non-trivial task. The underlying complexity emphasizes the need for quality control, and one way to measure ontologies is through ontology metrics. For a long time, the calculation of ontology metrics was merely a theoretical proposal: While there was no shortage of proposed ontology metrics, actual applications were mostly missing. That changed with the creation of NEOntometrics, a tool that implemented the majority of ontology metrics proposed in the literature. While it is now possible to calculate large amounts of ontology metrics, it also revealed that the calculation alone does not make the metrics useful (yet). In NEOntometrics alone, there are over 160 ontology metrics - a careful selection for the given use case is crucial. This position paper argues for a selection process for ontology metrics. It first presents core questions for identifying the underlying ontology requirements and then guides users to identify the correct attributes and their associated measures.

Place, publisher, year, edition, pages
SciTePress, 2023
Series
ICEIS, E-ISSN 2184-4992
Keywords
Knowledge Engineering, NEOntometrics, Ontology Metrics, Ontology Quality, OntoMetrics
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-63371 (URN)10.5220/0011983800003467 (DOI)2-s2.0-85160688922 (Scopus ID)978-989-758-648-4 (ISBN)
Conference
25th International Conference on Enterprise Information Systems, April 24-26, 2023, Prague, Czech Republic
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved
Würtz, M.-O. -. & Sandkuhl, K. (2023). Content and Evaluation of an Enterprise Reference Architecture for Demand-Responsive Public Transport. In: Y. Bie, Kun Gao, R. J. Howlett, L. C. Jain (Ed.), Smart Transportation Systems 2023: Proceedings of 6th KES-STS International Symposium. Paper presented at 6th KES International Symposium on Smart Transportation Systems, KES STS 2023 Rome, 14 June 2023 through 16 June 2023 (pp. 125-137). Springer
Open this publication in new window or tab >>Content and Evaluation of an Enterprise Reference Architecture for Demand-Responsive Public Transport
2023 (English)In: Smart Transportation Systems 2023: Proceedings of 6th KES-STS International Symposium / [ed] Y. Bie, Kun Gao, R. J. Howlett, L. C. Jain, Springer, 2023, p. 125-137Conference paper, Published paper (Refereed)
Abstract [en]

Demand-responsive public transport, such as new mobility solutions (NMS) like car sharing, city bikes, call taxis or e-scooters, have become increasingly popular in big cities. NMS have started to influence how people move around in urban areas, and they are also expected to impact public transport operators and their IT landscape. The paper aims to contribute to an understanding of this impact and uses enterprise architecture models as a basis for visualising and analysing relevant business and IT aspects. More precisely, the focus of the research presented herein lies on developing a reference enterprise architecture for integrating demand-responsive public transport into traditional supply-oriented public transport operators. More precisely, the aim of this paper is to evaluate the initial version of a reference enterprise architecture for demand-responsive public transport. The evaluation is based on expert interviews and focusses on the area of planning and resource utilisation.

Place, publisher, year, edition, pages
Springer, 2023
Series
Smart Innovation, Systems and Technologies, ISSN 2190-3018, E-ISSN 2190-3026 ; 256
Keywords
Demand-responsive public transport, Enterprise architecture, New mobility services, Public transport, Reference architecture, Mobility service, Mobility solutions, New mobility service, Public transport operators, Urban areas, Urban transportation
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:hj:diva-62184 (URN)10.1007/978-981-99-3284-9_12 (DOI)2-s2.0-85164731478 (Scopus ID)978-981-99-3283-2 (ISBN)978-981-99-3284-9 (ISBN)
Conference
6th KES International Symposium on Smart Transportation Systems, KES STS 2023 Rome, 14 June 2023 through 16 June 2023
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-10-27Bibliographically approved
Würtz, M.-O. -. & Sandkuhl, K. (2023). Enterprise Architecture for Integration of Demand-Responsive Services in Public Transport. Complex Systems Informatics and Modeling Quarterly, 2023(34), 62-83, Article ID 189.
Open this publication in new window or tab >>Enterprise Architecture for Integration of Demand-Responsive Services in Public Transport
2023 (English)In: Complex Systems Informatics and Modeling Quarterly, ISSN 2255-9922, Vol. 2023, no 34, p. 62-83, article id 189Article in journal (Refereed) Published
Abstract [en]

The popularity of new mobility services (NMS), like car sharing, urban bikes or e-scooters, is increasing in many urban areas. In comparison to traditional supply-oriented public transport with defined timetables and transportation routes, NMS are demand-responsive services aiming to meet the ad-hoc needs of users. NMS have started to influence the way people move in urban areas, and they also have an impact on public transport operators which begin to offer their own NMS or integrate the services of established NMS operators. This article investigates the changes implied by NMS for the enterprise architecture (EA) of public transport operators. The paper presents and evaluates an EA approach for integrating demand-responsive services into traditional supply-oriented public transport. We propose a partial enterprise architecture as an extension of the established ITVU core model, which is an established reference model in the public transportation domain. Although EA management is recognised as relevant for public transport companies, there is a lack of such an extension addressing NMS integration. The contribution of this article are to (1) offer an overview of the state of research in enterprise architectures for public transport, (2) make a case study for illustrating the challenges and requirements of NMS integration, (3) provide a first version of the partial (core) reference EA for integration of NMS including an initial evaluation.

Place, publisher, year, edition, pages
Riga Technical University, 2023
Keywords
Demand-Oriented Public Transport, Demand-Responsive Service, Enterprise Architecture, New Mobility Services, Public Transport, Reference Enterprise Architecture, Supply-Oriented Public Transport, Supply-Oriented Transport
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hj:diva-62346 (URN)10.7250/csimq.2023-34.03 (DOI)2-s2.0-85168011571 (Scopus ID)POA;;900533 (Local ID)POA;;900533 (Archive number)POA;;900533 (OAI)
Available from: 2023-08-30 Created: 2023-08-30 Last updated: 2023-08-30Bibliographically approved
Reiz, A. & Sandkuhl, K. (2023). Evolution of computational ontologies: Assessing development processes using metrics. In: F. Coenen, A. Fred, D. Aveiro, J. Dietz, J. Bernardino, E. Masciari, & J. Filipe (Ed.), Knowledge Discovery, Knowledge Engineering and Knowledge Management: 14th International Joint Conference, IC3K 2022, Valletta, Malta, October 24–26, 2022, Revised Selected Papers. Paper presented at 14th International Joint Conference, IC3K 2022, Valletta, Malta, October 24–26, 2022 (pp. 217-238). Cham: Springer
Open this publication in new window or tab >>Evolution of computational ontologies: Assessing development processes using metrics
2023 (English)In: Knowledge Discovery, Knowledge Engineering and Knowledge Management: 14th International Joint Conference, IC3K 2022, Valletta, Malta, October 24–26, 2022, Revised Selected Papers / [ed] F. Coenen, A. Fred, D. Aveiro, J. Dietz, J. Bernardino, E. Masciari, & J. Filipe, Cham: Springer, 2023, p. 217-238Conference paper, Published paper (Refereed)
Abstract [en]

Ontologies serve as a bridge between humans and computers. They encode domain knowledge to connect federated data sources or infer tacit knowledge. While the technology used in ontologies is widely adopted and can be considered mature, much has still to be learned about their evolution and development, as understanding ontology evolution could help ensure better quality control and provide knowledge engineers with helpful modeling and selection guidelines. This paper examines the evolution of computational ontologies using ontology metrics. The authors hypothesize that ontologies follow a similar development pattern. This assumption is broken down into five hypotheses and tested against historical metric data from 69 dormant ontologies and 7,053 versions. The research proved that the development processes of ontologies are highly diverse. While a slight majority of ontologies confirm some of the hypotheses, accepting them as a general rule is not possible. Further, the data revealed that most ontologies have disruptive change events for most measured attributes. These disruptive events are examined regarding their occurrences, combinations, and sizes. 

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1842
Keywords
NEOntometrics, Ontology evaluation, Ontology evolution, Ontology metrics, Owl, Rdf, Domain Knowledge, Quality control, Resource Description Framework (RDF), Uncertainty analysis, Computational ontology, Development process, Neontometric, Ontology evaluations, Ontology's, Ontology
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-63372 (URN)10.1007/978-3-031-43471-6_10 (DOI)2-s2.0-85174322353 (Scopus ID)9783031434709 (ISBN)9783031434716 (ISBN)
Conference
14th International Joint Conference, IC3K 2022, Valletta, Malta, October 24–26, 2022
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7431-8412

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