More Web Proxy on the site http://driver.im/

research-article

Augmenting model-based systems engineering with knowledge

Authors:

Luis Palacios Medinacelli,

Florian Noyrit,

Chokri MraidhaAuthors Info & Claims

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

Pages 351 - 358

https://doi.org/10.1145/3550356.3561548

Published: 09 November 2022 Publication History

Abstract

This article presents a general approach for the integration of Knowledge Bases into Model-Based Systems Engineering tools. In existing tools, domain-specific modeling languages are well supported. However when it comes to enforcing design constraints, existing approaches are verbose, it is difficult to be complete and consistent, and the reuse of knowledge is only possible in a limited way (mainly through model libraries). Furthermore, current tools usually lack or have limited capability to detect semantic errors, ability to evaluate the models with respect to formal expert knowledge, and the ability to understand what is being designed. Our work addresses these limitations through the semantic annotation of UML models in Papyrus (an MBSE Tool), to attach domain-specific semantics to the models. This integration enables not only reasoning capabilities over the annotated models, but the models can be shared with semantic-compatible tools and stakeholders. Moreover, the models can reuse and integrate knowledge generated outside the tooling environment. The approach's feasibility is demonstrated through an implementation that defines a technology stack, with emphasis on the mapping of UML elements and its counterparts in the ontology. We address the coherence and preservation of the semantics throughout the transformation process, which enable the formalization of constraints coming from the UML's system design. Finally, we illustrate the reasoning capabilities by evaluating expert knowledge via SPARQL queries and SWRL rules.

References

[1]

Colin Atkinson and Kilian Kiko. 2005. A detailed comparison of UML and OWL. None (2005).

[2]

Franz Baader. 2003. Appendix: description logic terminology. The Description logic handbook: Theory, implementation, and applications (2003), 485--495.

[3]

Franz Baader, Diego Calvanese, Deborah McGuinness, Peter Patel-Schneider, Daniele Nardi, et al. 2003. The description logic handbook: Theory, implementation and applications. Cambridge university press.

[4]

Mohammed Diab, Aliakbar Akbari, Muhayy Ud Din, and Jan Rosell. 2019. PMK---a knowledge processing framework for autonomous robotics perception and manipulation. Sensors 19, 5 (2019), 1166.

[5]

Dov Dori and Hillary Sillitto. 2017. What is a system? An ontological framework. Systems Engineering 20, 3 (2017), 207--219.

Digital Library

[6]

Mohamad Eid, Ramiro Liscano, and Abdulmotaleb El Saddik. 2007. A universal ontology for sensor networks data. In 2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications. IEEE, 59--62.

[7]

Maged Elaasar and Nicolas Rouquette. 2022. http://www.opencaesar.io/oml/

[8]

Hicham Elasri and Abderrahim Sekkaki. 2013. Semantic integration process of business components to support information system designers. arXiv preprint arXiv:1302.1393 (2013).

[9]

Colomb R. et al. 2006. The object management group ontology definition metamodel. In Ontologies for software engineering and software technology. Springer, 217--247.

[10]

Manzoor S. et al. 2021. Ontology-based knowledge representation in robotic systems: A survey oriented toward applications. Applied Sciences 11, 10 (2021), 4324.

[11]

Schlenoff C. et al. 2012. An IEEE standard ontology for robotics and automation. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 1337--1342.

[12]

Vanherpen K. et al. 2016. Ontological reasoning for consistency in the design of cyber-physical systems. In 2016 1st International Workshop on Cyber-Physical Production Systems (CPPS). IEEE, 1--8.

[13]

Ronald E Giachetti. 2015. Evaluation of the DoDAF meta-model's support of systems engineering. Procedia Computer Science 61 (2015), 254--260.

[14]

Niklas Hallberg, Erland Jungert, and Sofie Pilemalm. 2014. Ontology for systems development. International journal of software engineering and knowledge engineering 24, 03 (2014), 329--345.

[15]

Olszewska Joanna Isabelle et al. 2017. Ontology for autonomous robotics. In 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, 189--194.

[16]

Eeva Järvenpää, Niko Siltala, Otto Hylli, and Minna Lanz. 2019. The development of an ontology for describing the capabilities of manufacturing resources. Journal of Intelligent Manufacturing 30, 2 (2019), 959--978.

Digital Library

[17]

David Martín-Lammerding, Alberto Córdoba, José Javier Astrain, Pablo Medrano, and Jesús Villadangos. 2020. An ontology-based system to collect WSN-UAS data effectively. IEEE Internet of Things Journal 8, 5 (2020), 3636--3652.

[18]

Marc H Meyer and Michael H Zack. 1996. The design and development of information products. MIT Sloan Management Review 37, 3 (1996), 43.

[19]

Vitaliy Mezhuyev. 2014. Ontology based development of domain specific languages for systems engineering. In 2014 International Conference on Computer and Information Sciences (ICCOINS). IEEE, 1--6.

[20]

Meriem Mejhed Mkhinini, Ouassila Labbani-Narsis, and Christophe Nicolle. 2020. Combining UML and ontology: An exploratory survey. Computer Science Review 35 (2020), 100223.

Digital Library

[21]

Haradhan Mohajan. 2016. A comprehensive analysis of knowledge management cycles. (2016).

[22]

Alberto Olivares-Alarcos et al. 2019. A review and comparison of ontology-based approaches to robot autonomy. The Knowledge Engineering Review 34 (2019).

[23]

Fernando Silva Parreiras. 2011. Marrying model-driven engineering and ontology technologies: The TwoUse approach. (2011).

[24]

E. Prestes, S. Fiorini, and J. Carbonera. 2014. Core Ontology for Robotics and Automation.

[25]

Vitor Fortes Rey Sandro Rama Fiorini. 2015. IEEE1872-owl. https://github.com/srfiorini/IEEE1872-owl. OWL specification of the Core Ontology for Robotics and Automation (CORA) and other IEEE 1872-2015 ontologies.

[26]

Moritz Tenorth and Michael Beetz. 2017. Representations for robot knowledge in the KnowRob framework. Artificial Intelligence 247 (2017), 151--169.

[27]

LC Van Ruijven. 2013. Ontology for systems engineering. Procedia Computer Science 16 (2013), 383--392.

[28]

Lan Yang et al. 2017. Towards a methodology for systems engineering ontology development---An ontology for system life cycle processes. In 2017 IEEE International Systems Engineering Symposium (ISSE). IEEE, 1--7.

[29]

Lan Yang, Kathryn Cormican, and Ming Yu. 2019. Ontology-based systems engineering: A state-of-the-art review. Computers in Industry 111 (2019), 148--171.

Cited By

Göbel SLämmel RCombemale BWimmer MChechik MEgyed A(2024)Model-Based Trust Analysis of LLM ConversationsProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3687809(602-610)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3687809
Huang YDhouib SMedinacelli LMalenfant J(2023)Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)10.1109/ICPS58381.2023.10128003(1-8)Online publication date: 8-May-2023
https://doi.org/10.1109/ICPS58381.2023.10128003
Ollier GAdedjouma MGerasimou SMraidha C(2023)An Ontological Approach for the Dependability Analysis of Automated Systems2023 26th Euromicro Conference on Digital System Design (DSD)10.1109/DSD60849.2023.00087(593-601)Online publication date: 6-Sep-2023
https://doi.org/10.1109/DSD60849.2023.00087
Show More Cited By

Index Terms

Augmenting model-based systems engineering with knowledge
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
2. Theory of computation
  1. Semantics and reasoning

Recommendations

How are UML class diagrams built in practice? A usability study of two UML tools: Magicdraw and Papyrus
Highlights
- This paper analyzes how UML class diagrams are built by analyzing 12 h of video recordings of students using MagicDraw and Papyrus to draw example class ...
Abstract
Software modeling is a key activity in software development, especially when following any kind of Model Driven Software Engineering (MDSE) process. In this context, standard modeling languages, like the Unified Modeling Language (UML),...
Model-driven engineering with domain-specific meta-modelling languages

Domain-specific modelling languages are normally defined through general-purpose meta-modelling languages like the MOF. While this is satisfactory for many model-driven engineering (MDE) projects, several researchers have identified the need for domain-...
RDFS(FA): Connecting RDF(S) and OWL DL

Semantic Web (SW) languages are supposed to be compatible with each other in a meaningful way, so as to facilitate machine understanding. Recent research, however, shows that the semantics of the standard SW annotation language RDF (as well as its ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

October 2022

1003 pages

ISBN:9781450394673

DOI:10.1145/3550356

Conference Chairs:
Thomas Kühn
Karlsruhe Institute of Technology, Germany
,
Vasco Sousa
University of Montréal, Canada

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

In-Cooperation

Univ. of Montreal: University of Montreal
IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 November 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MODELS '22

Sponsor:

SIGSOFT

MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems

October 23 - 28, 2022

Quebec, Montreal, Canada

Acceptance Rates

Overall Acceptance Rate 144 of 506 submissions, 28%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
140
Total Downloads

Downloads (Last 12 months)37
Downloads (Last 6 weeks)2

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Göbel SLämmel RCombemale BWimmer MChechik MEgyed A(2024)Model-Based Trust Analysis of LLM ConversationsProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3687809(602-610)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3687809
Huang YDhouib SMedinacelli LMalenfant J(2023)Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)10.1109/ICPS58381.2023.10128003(1-8)Online publication date: 8-May-2023
https://doi.org/10.1109/ICPS58381.2023.10128003
Ollier GAdedjouma MGerasimou SMraidha C(2023)An Ontological Approach for the Dependability Analysis of Automated Systems2023 26th Euromicro Conference on Digital System Design (DSD)10.1109/DSD60849.2023.00087(593-601)Online publication date: 6-Sep-2023
https://doi.org/10.1109/DSD60849.2023.00087
Subhi MNicolas WRenard ARomero GOuederni MChaari L(2023)How AI can Advance Model Driven Engineering Method ?Intelligent Systems and Pattern Recognition10.1007/978-3-031-46338-9_9(113-125)Online publication date: 5-Nov-2023
https://doi.org/10.1007/978-3-031-46338-9_9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents