Soft Computing

, Volume 21, Issue 20, pp 6105–6119 | Cite as

Development supporting framework of architectural descriptions using heavy-weight ontologies with fuzzy-semantic similarity

  • Mye Sohn
  • Sunghwan Jeong
  • Taehoon Kim
  • Hyun Jung Lee
Methodologies and Application


This paper proposes a framework to support the development of architectural descriptions that have DM2-compliance data and are constructed and integrated under considering of the consistency between views as components of the viewpoints with different perspectives of the system. To do so, we developed two heavy-weight ontologies: DM2 Data Dictionary ontology containing whole things of DoDAF DM2 terms such as the definitions, aliases, and mapping relations to DoDAF Models and architectural description ontology expresses all outcomes of efforts to develop the architectural descriptions in a case structure. Based on the ontologies, our framework implements a fuzzy-semantic similarity measure to identify the appropriate views using case-based reasoning to meet the goal as a solution of the architecture to solve a problem. Furthermore, we performed data validation to check the DM2 compliance based on DM2 Data Dictionary ontology. To verify the superiority of our framework, we perform the three kinds of evaluations. First, we evaluate the effectiveness of the fuzzy-semantic similarity measure. Second, we verify the completeness of relationships of the data entities and attributes required to develop the architectural descriptions. Finally, we show the performance of the DM2 Data Dictionary ontology from a DM2 compliance. As a result, the experimental results verified the effectiveness and superiority of the proposed framework.


Fuzzy-semantic similarity measure  Heavy-weight ontology DoDAF Meta-Model (DM2) Case-based reasoning Views 



This research was partially supported by the IT R&D program of MKE/KEIT [No. 10041788, Development of Smart Home Service based on Advanced Context-Awareness] and partially supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “IT Consilience Creative Program” (IITP-2015-R0346-15-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion).

Compliance with ethical standards

Conflict of interest

None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.


  1. Allemang D, Hodgson R, Polikoff I (2005) FEA reference model ontologies: FEA-RMO version 1.1.
  2. Arseniev M (2004) Enterprise architecture implementation: practical steps using open source tools.
  3. Broscheit S, Poesio M, Ponzetto SP, Rodriguez KJ, Romano L, Uryupina O, Zanoli R (2010) BART: a multilingual anaphora resolution system. In: Proceedings of the 5th international workshop on semantic evaluation, pp 104–107Google Scholar
  4. Chen Z, Pooley R (2009) Rediscovering Zachman framework using ontology from a requirement engineering perspective. In: Proceedings of the 33rd annual IEEE international computer software and applications conference (COMPSAC), vol 2, pp 3–8, IEEEGoogle Scholar
  5. Davis K (2013) Development of an architecture framework for portfolios of sustainable technology project (Doctoral dissertation, The George Washington University)Google Scholar
  6. Department of Defense (2015) Department of Defense Architecture Framework 2.02, Change 1, online available at
  7. DoD Deputy Chief Information Officer (2011) DoDAF v2.02, Data Dictionary and model mappings.
  8. Ehrig M, Haase P, Hefke M, Stojanovic N (2005) Similarity for ontologies-a comprehensive framework. In: Proceedings of European conference on information systems (ECIS), p 127Google Scholar
  9. Handley HA (2012) Incorporating the NATO human view in the DoDAF 2.0 meta model. Syst Eng 15(1):108–117CrossRefGoogle Scholar
  10. Haruechaiyasak C, Shyu ML, Chen SC (2002) Web document classification based on fuzzy association. In: Proceedings of 26th annual international computer software and applications conference (COMSAC), pp 487–492, IEEEGoogle Scholar
  11. Haukaas F (2010) DoD architecture framework (DoDAF) relevance to JTIC testing.
  12. Hause M (2010) The unified profile for DoDAF/MODAF (UPDM) enabling systems of systems on many levels. In: Proceedings of the 4th annual systems conference, pp 426–431, IEEEGoogle Scholar
  13. Ho LJ, Kim H, Lee J (1993) Information retrieval based on conceptual distance in IS-A hierarchies. J Doc 49(2):188–207CrossRefGoogle Scholar
  14. Ideas Group (2011). DoDAF Meta Model (DM2).
  15. Jiang JY, Tsai SC, Lee SJ (2012) FSKNN: multi-label text categorization based on fuzzy similarity and k nearest neighbors. Expert Syst Appl 39(3):2813–2821CrossRefGoogle Scholar
  16. Kang D, Lee J, Choi S, Kim K (2010) An ontology-based enterprise architecture. Expert Syst Appl 37(2):1456–1464CrossRefGoogle Scholar
  17. Kilpeläinen T, Nurminen M (2007) Applying genre-based ontologies to enterprise architecture. In: Proceedings of the 18th Australasian conference on information systems, p 65Google Scholar
  18. Kirikova M, Penicina L, Gaidukovs A (2015) Ontology based linkage between enterprise architecture, processes, and time. In: Morzy T, Valduriez P, Bellatreche L (eds) Proceedings of the new trends in databases and information systems. Springer, New York, pp 382–391Google Scholar
  19. Kwon YM, Sohn M, Lee HJ (2012) The design and implementation of ontology for architecture framework (ONT-DAF) in military domain. Int J Control Autom 5(2):141–150Google Scholar
  20. Li L, Dou Y, Ge B, Yang K, Chen Y (2012) Executable system-or-systems architecting based on DoDAF meta-model. In: Proceeding of the 7th international conference on system of systems engineering (SoSE), pp 362–367, IEEEGoogle Scholar
  21. Liu F et al (2012) An intuitionistic fuzzy set model for concept similarity using ontological relations. In: Services computing conference (APSCC), 2012 IEEE Asia-Pacific. IEEE, pp 319–324Google Scholar
  22. Romero L, Gutierrez M, Caliusco ML (2013) A conceptualization of e-Assessment domain. In: Proceeding of 2013 8th Iberian conference on information systems and technologies (CISTI), pp 1–6, IEEEGoogle Scholar
  23. Sage AP, Lynch CL (1998) Systems integration and architecting: an overview of principles, practices, and perspectives. Syst Eng 1(3):176–227CrossRefGoogle Scholar
  24. Saraçoğlu R, Tütüncü K, Allahverdi N (2008) A new approach on search for similar documents with multiple categories using fuzzy clustering. Expert Syst Appl 34(4):2545–2554CrossRefGoogle Scholar
  25. Slimani T, BenYaghlane B, Mellouli K (2007) Une extension de mesure de similarité entre les concepts d’une ontologie. In: Proceeding of the international conference on sciences of electronic, technologies of information and telecommunications, pp 1–10Google Scholar
  26. Software Engineering Standards Committee (2000) IEEE recommended practice for architectural description of software-intensive systems. Technical Report IEEE Std 1471–2000, IEEE Computer SocietyGoogle Scholar
  27. Sohn M, Kang S, Lee HJ (2014) Integrated data model development framework for the architecture descriptions. J Internet Serv Inf Secur JISIS 4(4):91–102Google Scholar
  28. Song L, Ma J, Liu H, Lian L, Zhang D (2007) Fuzzy semantic similarity between ontological concepts. Proceedings of advances and innovations in systems, computing sciences and software engineering. Springer, Netherlands, pp 275–280CrossRefGoogle Scholar
  29. The North Atlantic Treaty Organization (NATO) (2010) NATO architecture framework.
  30. Wu Z, Palmer M (1994) Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on association for computational linguistics, Association for Computational Linguistics, pp 133–138Google Scholar
  31. Zhang L, Ma ZM (2012) ICFC: a method for computing semantic similarity among fuzzy concepts in a fuzzy ontology. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–8, IEEEGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Mye Sohn
    • 1
  • Sunghwan Jeong
    • 1
  • Taehoon Kim
    • 1
  • Hyun Jung Lee
    • 2
  1. 1.Department of Industrial EngineeringSungkyunkwan UniversitySuwonKorea
  2. 2.School of Integrated Technology, Yonsei Institute of Convergence TechnologyYonsei UniversityIncheonKorea

Personalised recommendations