Abstract
In Enterprise Architecture Management (EAM), rules, constraints, and principles guide and govern the Enterprise Architecture (EA). These can be formulated and verified in ontology-based enterprise architecture models. The automatic validation of EA principles relies on the knowledge available in the EA models. However, there is knowledge implicit in models that humans may understand but machines cannot. For example, relationships between model elements may be derived using derivation rules and domain knowledge. Formalizing derivation rules in an enterprise ontology, we can infer this implicit knowledge and make it available to the machine for reasoning. This research demonstrates the feasibility of using derivation rules to extract implicit knowledge from enterprise models allowing EA principles validation and supporting EAM. The research contribution is presented using a concrete real-world use case and implementing the derivation rules for the EA modeling standard ArchiMate.
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References
Afonina, V., Hinkelmann, K., Montecchiari, D.: Enriching enterprise architecture models with healthcare domain knowledge. In: Ruiz, M., Soffer, P. (eds.) Advanced Information Systems Engineering Workshops, CAiSE 2023. LNBIP, vol. 482, pp. 17–28. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34985-0_2
Ahlemann, F., Stettiner, E., Messerschmidt, M., Legner, C. (eds.): Strategic Enterprise Architecture Management: Challenges, Best Practices, and Future Developments. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24223-6
Antunes, G., Bakhshandeh, M., Mayer, R., Borbinha, J., Caetano, A.: Using ontologies for enterprise architecture integration and analysis. Complex Syst. Inform. Model. Q. 1(1), 1–23 (2014)
Caetano, A., et al.: Representation and analysis of enterprise models with semantic techniques: an application to ArchiMate, e3value and business model canvas. Knowl. Inf. Syst. 50(1), 315–346 (2017)
Dietz, J.L., Mulder, H.B.: Enterprise Ontology. A Human-Centric Approach to Understanding the Essence of Organisation. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38854-6
Feilmayr, C., Wöß, W.: An analysis of ontologies and their success factors for application to business. Data Knowl. Eng. 101, 1–23 (2016)
Fox, M.S., Gruninger, M.: Enterprise modeling. AI Mag. 19(3), 109 (1998)
Greefhorst, D., Proper, E.: Architecture Principles: The Cornerstones of Enterprise Architecture. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20279-7
Guerson, J., Sales, T.P., Guizzardi, G., Almeida, J.P.A.: OntoUML lightweight editor: a model-based environment to build, evaluate and implement reference ontologies. In: 2015 IEEE 19th International Enterprise Distributed Object Computing Workshop, pp. 144–147. ieeexplore.ieee.org, September 2015
Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., van der Merwe, A., Woitsch, R.: A new paradigm for the continuous alignment of business and it: combining enterprise architecture modelling and enterprise ontology. Comput. Ind. 79, 77–86 (2016)
Hinkelmann., K., Laurenzi., E., Martin., A., Montecchiari., D., Spahic., M., Thönssen., B.: ArchiMEO: a standardized enterprise ontology based on the ArchiMate conceptual model. In: Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, pp. 417–424. INSTICC, SciTePress (2020). https://doi.org/10.5220/0009000204170424
Hinkelmann, K., Laurenzi, E., Martin, A., Thönssen, B.: Ontology-based metamodeling. In: Dornberger, R. (ed.) Business Information Systems and Technology 4.0. SSDC, vol. 141, pp. 177–194. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74322-6_12
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M., et al.: SWRL: a semantic web rule language combining OWL and RuleML. W3C Member Submission 21(79), 1–31 (2004)
Hubert, C., Lemons, D.: APQC’s levels of knowledge management maturity. APQC 2010, 1–5 (2010)
Kappel, G., et al.: Lifting metamodels to ontologies: a step to the semantic integration of modeling languages. In: Nierstrasz, O., Whittle, J., Harel, D., Reggio, G. (eds.) MODELS 2006. LNCS, vol. 4199, pp. 528–542. Springer, Heidelberg (2006). https://doi.org/10.1007/11880240_37
Karagiannis, D., Bork, D., Utz, W.: Metamodels as a conceptual structure: some semantical and syntactical operations. In: Bergener, K., Räckers, M., Stein, A. (eds.) The Art of Structuring, pp. 75–86. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06234-7_8
Karagiannis, D., Buchmann, R.A.: A proposal for deploying hybrid knowledge bases: the ADOxx-to-GraphDB interoperability case. In: Hawaii International Conference on System Sciences 2018 (HICSS-51) (2018)
Karagiannis, D., Buchmann, R.A., Bork, D.: Managing consistency in multi-view enterprise models: an approach based on semantic queries. In: European Conference on Information Systems (2016)
Karagiannis, D., Woitsch, R.: Knowledge engineering in business process management. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 2. IHIS, pp. 623–648. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-642-45103-4_26
Knublauch, H., Kontokostas, D.: Shapes constraint language (SHACL). Technical report, W3C, July 2017. https://www.w3.org/TR/shacl/
Kritikos, K., Laurenzi, E., Hinkelmann, K.: Towards business-to-IT alignment in the cloud. In: Mann, Z.Á., Stolz, V. (eds.) ESOCC 2017. CCIS, vol. 824, pp. 35–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-79090-9_3
Laurenzi, E., Hinkelmann, K., van der Merwe, A.: An agile and ontology-aided modeling environment. In: Buchmann, R.A., Karagiannis, D., Kirikova, M. (eds.) PoEM 2018. LNBIP, vol. 335, pp. 221–237. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02302-7_14
Laurenzi, E., Hinkelmann, K., Montecchiari, D., Goel, M.: Agile visualization in design thinking. In: Dornberger, R. (ed.) New Trends in Business Information Systems and Technology. SSDC, vol. 294, pp. 31–47. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-48332-6_3
Marosin, D., van Zee, M., Ghanavati, S.: Formalizing and modeling enterprise architecture (EA) principles with goal-oriented requirements language (GRL). In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 205–220. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_13
Montecchiari, D.: Ontology-based validation of enterprise architecture principles in enterprise models. In: BIR 2021 Workshops and Doctoral Consortium, co-located with 20th International Conference on Perspectives in Business Informatics Research, Vienna, Austria, 22–24 September 2021. http://ceur-ws.org/Vol-2991/
Montecchiari, D., Hinkelmann, K.: Towards ontology-based validation of EA principles. In: Barn, B.S., Sandkuhl, K. (eds.) The Practice of Enterprise Modeling, PoEM 2022. LNBIP, vol. 456, pp. 66–81. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21488-2_5
Olivé, A.: Derivation rules in object-oriented conceptual modeling languages. In: Eder, J., Missikoff, M. (eds.) CAiSE 2003. LNCS, vol. 2681, pp. 404–420. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45017-3_28
OMG: Meta object facility (MOF) core specification, version 2.4.2. Technical report, Object Management Group (2014). https://www.omg.org/spec/MOF/2.4.2/PDF
Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. (TODS) 34(3), 1–45 (2009)
Peter, M., Montecchiari, D., Hinkelmann, K., Gatziu Grivas, S.: Ontology-based visualization for business model design. In: Grabis, J., Bork, D. (eds.) PoEM 2020. LNBIP, vol. 400, pp. 244–258. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63479-7_17
Ross: Basic RuleSpeak Guidelines. Do’s and Don’ts in Expressing Natural-Language (2009)
Santana, A., Simon, D., Fischbach, K., de Moura, H.: Combining network measures and expert knowledge to analyze enterprise architecture at the component level. In: 2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), pp. 1–10, September 2016
Smajevic, M., Hacks, S., Bork, D.: Using knowledge graphs to detect enterprise architecture smells. In: Serral, E., Stirna, J., Ralyté, J., Grabis, J. (eds.) PoEM 2021. LNBIP, vol. 432, pp. 48–63. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91279-6_4
Sportelli, F., Franconi, E.: Formalisation of ORM derivation rules and their mapping into OWL. In: Debruyne, C., et al. (eds.) OTM 2016. LNCS, vol. 10033, pp. 827–843. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48472-3_52
Staworko, S., Boneva, I., Gayo, J.E.L., Hym, S., Prud’Hommeaux, E.G., Solbrig, H.: Complexity and expressiveness of ShEx for RDF. In: 18th International Conference on Database Theory, ICDT 2015 (2015)
The Open Group: TOGAF 9 - The Open Group Architecture Framework, vol. 9 (2009)
The Open Group: ArchiMate 3.2 Specification (2023)
Uschold, M., King, M., Moralee, S., Zorgios, Y.: The enterprise ontology. Knowl. Eng. Rev. 13, 31–89 (1998). Special Issue on Putting Ontologies to Use
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Montecchiari, D., Hinkelmann, K. (2023). Validating Enterprise Architecture Principles Using Derivation Rules and Domain Knowledge. In: Hinkelmann, K., López-Pellicer, F.J., Polini, A. (eds) Perspectives in Business Informatics Research. BIR 2023. Lecture Notes in Business Information Processing, vol 493. Springer, Cham. https://doi.org/10.1007/978-3-031-43126-5_18
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