Ontology-based systems engineering: A state-of-the-art review
Introduction
An ontology is a formal, explicit specification of a shared conceptualization (Gruber, 1995; Borst et al., 1997; Studer et al., 1998). Much research has been conducted on improving systems engineering (SE) by using ontologies in the last two decades (Honour and Valerdi, 2014; Hallberg et al., 2014; Sarder and Ferreira, 2007). Not only can ontologies make the nature and structure of engineered systems and their components explicit, but they can also help different stakeholders better understand the complexities inherent in large engineered systems and their socio-technical environments (Mezhuyev, 2014). However, despite the continuous focus on merging ontologies into SE, there is no comprehensive and systematic interpretation of how ontologies influence and support SE, or what ontologies are established to best represent the SE domain knowledge. Therefore, we employed a systematic literature review (SLR) methodology (Kitchenham et al., 2015) to identify, evaluate, interpret and synthesize the available literature to answer particular research questions and establish the state of the art of ontology-based systems engineering (OBSE) with in-depth analysis.
In recent years, several reviews have been conducted that focus on different topics of SE, such as the future challenges for service-oriented SE (Gu and Lago, 2009) and model-based systems engineering (MBSE) tools (Rashid et al., 2015). Furthermore, to tackle emerging challenges within the system of systems (SOS) engineering, several reviews have synthesized the approaches for architecture (Klein and van Vliet, 2013; Guessi et al., 2015a), knowledge representation (Abdalla et al., 2015), quality attributes (Bianchi et al., 2015), system integration (Vargas et al., 2016) and requirement engineering (Vierhauser et al., 2016; de Lima et al., 2017). However, a manifest research gap still exists in the literature regarding the state of the art of ontologies in SE.
Moreover, much literature has reported the challenges and problems that SE is facing. Firstly, current SE domain knowledge shows a poorly structured representation, which is due to the high level of fragmentation in the SE discourse (Warfield, 2003). Secondly, it is heuristic in origin, which implies that the performance of SE relies heavily on personal experiences (Chourabi et al., 2010). This results in a lack of consistent evaluation of SE standards, both in the forms of handbooks and meta-models. In particular, the standards are limited to human-readable descriptions and are not computer-interpretable (Yang et al., 2017). Thus, they are shared as textual documents which are not the format of choice for semantic representations (Di Maio, 2011). Furthermore, the vocabulary used to build the SE meta-models is not the most common in the SE community, and researchers, in turn, argue that these meta-models need improvement in terms of their semantics (Giachetti, 2015). Finally, inefficient collaborations caused by the misunderstanding and misinterpretation commonly exist in SE projects, because SE involves stakeholders with different competencies and skills who cannot always communicate efficiently (Sarder and Ferreira, 2007).
To address the above challenges, ontologies are introduced in SE (Sarder and Ferreira, 2007). Formal ontologies are lauded for providing a shared understanding among people, organizations, and systems to bridge the gaps. Notably, it has been proven that ontologies harmonize the interpretation of multiple standards and different models (Pardo et al., 2012). Ontologies, as a result of a terminology agreement within a users’ community, are extolled for avoiding ambiguity and providing a widely accepted and consistent vocabulary (Sillitto, 2011; Rousseau et al., 2016; Roussey et al., 2011). Moreover, ontologies are used to facilitate semantic interoperability between humans as well as between humans and computers (Bittner et al., 2005).
Dori (2016) indicates that “systems science and engineering are in need of a well-defined foundational, universal, general, necessary and sufficient ontology that would underpin concepts and terms it uses in order for them to be precise and unambiguous.” Therefore, it will be of significant value to have a clear understanding of the state of the art of OBSE. This paper aims to systematically review the available literature to investigate and better understand how ontologies influence and support SE and to what extent they have been applied in SE. This review contributes to the SE community in several ways. It presents a holistic perspective on how ontologies are applied to different SE knowledge areas. It gathers evidence from primary studies to demonstrate the contribution of ontologies on SE problems. It illuminates avenues for future research directions for OBSE.
This paper proceeds as follows. Section 2 provides the research foundation for this review by clarifying the scope of the SE knowledge domain. It discusses the rationale and importance of this review and introduces the fundamentals of ontologies relevant to this paper. Section 3 presents the research methodology and describes the review process. Section 4 analyses the results and discusses each research question in detail. Section 5 provides insights, a discussion of related work, limitations of this study and a future roadmap. Finally, Section 6 concludes the paper.
Section snippets
Research Foundation
This section provides a classification of knowledge areas in the SE body of knowledge and introduces ontologies. It also addresses the rationale of why SE needs ontologies.
Research Methodology
In this section, the SLR method used in conducting this state-of-the-art review is set forth, followed by a definition of the research questions.
Results and Analysis
Our overarching research question is, how do ontologies support SE? However, during the review process, we came across many papers that discussed how SE approaches had been applied to advance ontology design and development, contrary to what we expected. We, therefore, excluded these papers. We also excluded papers if they discussed Ontology in terms of philosophy. For example, Oliga (1988) clarifies the underlying metatheoretical assumptions for the methodological foundations of systems
Discussion
In this section, we discuss the related work and compare our findings with them. Also, we try to form a roadmap for the future direction of OBSE. It is an initial step and needs evaluation and improvement. Furthermore, this review has some limitations which we will discuss and finally we provide some recommendations for further research.
Conclusion
This paper reveals the current state of the art of the OBSE and provides a detailed account of key SE knowledge areas supported by ontologies. Through this review, we identified the knowledge areas of SE supported by ontologies, ascertained the purposes of using ontologies, succinctly describe the existing SE ontologies, assessed their adoption of ontology engineering techniques, and outlined an agenda for future research.
The results indicate that ontology is applied across many different SE
Acknowledgements
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We would like to acknowledge and thank all those who have helped us by assessing manuscripts submitted for publication. Professor Harinder Jagdev, Audrey Fayne, Clare O’Dwyer, Suzana Sampaio, Séamus Caulfield, and Chandrasekhar Dhanapathi.
Lan Yang is a PhD candidate in the College of Engineering & Informatics at the National University of Ireland, Galway (NUI Galway). Lan received her masters’ degree in Engineering Science at NUI Galway and in Management Science and Engineering at Tsinghua University. Her bachelor’s degree is in Industrial Engineering at Beijing Jiaotong University. Lan is a member of the International Council on Systems Engineering (INCOSE) Knowledge Management & Ontologies Working Group. Her research interests
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Cited by (0)
Lan Yang is a PhD candidate in the College of Engineering & Informatics at the National University of Ireland, Galway (NUI Galway). Lan received her masters’ degree in Engineering Science at NUI Galway and in Management Science and Engineering at Tsinghua University. Her bachelor’s degree is in Industrial Engineering at Beijing Jiaotong University. Lan is a member of the International Council on Systems Engineering (INCOSE) Knowledge Management & Ontologies Working Group. Her research interests are knowledge management, information systems, ontologies, systems engineering, and project management.
Dr Kathryn Cormican leads research in the area of Technology Innovation Management in the College of Engineering & Informatics at NUI Galway. Her work is multidisciplinary and applied, and she works closely with industry to identify and prioritise requirements, co-develop solutions and validate and implement new models and systems. She has published over 120 peer-reviewed papers in leading journals and conferences. She is internationally recognised for her contribution to research and has won many best paper awards. Kathryn serves on several editorial and scientific committees for leading journals and conferences in her field. She also works closely with many leading organizations and small & medium-sized enterprises helping them to diagnose, develop and deploy new processes and systems.
Prof. Dr Ming Yu is an associated professor and the leader of the Health Care Services Research Center in the Department of Industrial Engineering at Tsinghua University. He received his PhD in Industrial Engineering, Business Process Reengineering, and Change Management at NUI Galway. His research interests are knowledge management, enterprise integration, and project management. Ming is a member of the INCOSE Knowledge Management & Ontologies Working Group.