Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Model-Based Construction of Enterprise Architecture Knowledge Graphs

  • Conference paper
  • First Online:
Enterprise Design, Operations, and Computing (EDOC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13585))

Abstract

Enterprise Architecture offers guidelines for the coherent, model-based design and management of enterprises. EA models provide a layered, integrated, and cohesive representation of the enterprise, enabling communication, analysis, and decision making. With the increasing size of EA models, automated analysis becomes essential. However, advanced model analysis is neither incorporated in current EA methods like ArchiMate nor supported by existing EA tools like Archi. Knowledge Graphs (KGs) can effectively organize and represent knowledge and enable reasoning to utilize this knowledge, e.g., for decision support. This paper introduces a model-based Enterprise Architecture Knowledge Graph (EAKG) construction method and shows how starting from ArchiMate models, an initially derived EAKG can be further enriched by EA-specific and graph characteristics-based knowledge. The introduced EAKG entails new representation and reasoning methods applicable to EA knowledge. As a proof of concept, we present the results of a first Design Science Research Cycle aiming to realize an Archi plugin for the EAKG that enables analysis of EA Smells within ArchiMate models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.archimatetool.com/, last accessed: 15.08.2022.

  2. 2.

    https://swc-public.pages.rwth-aachen.de/smells/ea-smells/, accessed: 11.05.2022.

  3. 3.

    EAKG Github repository: https://github.com/borkdominik/archi-kganalysis-plugin.

  4. 4.

    Archi plugins: https://www.archimatetool.com/plugins/, accessed 02.05.2022.

References

  1. Ahlemann, F., Stettiner, E., Messerschmidt, M., Legner, C.: Strategic Enterprise Architecture Management: Challenges, Best Practices, and Future Developments. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24223-6

    Book  Google Scholar 

  2. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  3. Bakhshadeh, M., Morais, A., Caetano, A., Borbinha, J.: Ontology transformation of enterprise architecture models. In: Camarinha-Matos, L.M., Barrento, N.S., Mendonça, R. (eds.) DoCEIS 2014. IAICT, vol. 423, pp. 55–62. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54734-8_7

    Chapter  Google Scholar 

  4. Barbosa, A., Santana, A., Hacks, S., Stein, N.V.: A taxonomy for enterprise architecture analysis research. In: 21st International Conference on Enterprise Information Systems, vol. 2, pp. 493–504. SciTePress (2019)

    Google Scholar 

  5. Bellomarini, L., Fakhoury, D., Gottlob, G., Sallinger, E.: Knowledge graphs and enterprise AI: the promise of an enabling technology. In: 35th IEEE International Conference on Data Engineering, pp. 26–37. IEEE (2019)

    Google Scholar 

  6. Bellomarini, L., Sallinger, E., Vahdati, S.: Chapter 2 Knowledge graphs: the layered perspective. In: Janev, V., Graux, D., Jabeen, H., Sallinger, E. (eds.) Knowledge Graphs and Big Data Processing. LNCS, vol. 12072, pp. 20–34. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53199-7_2

    Chapter  Google Scholar 

  7. Bernasconi, A., Canakoglu, A., Ceri, S.: From a conceptual model to a knowledge graph for genomic datasets. In: Laender, A.H.F., Pernici, B., Lim, E.-P., de Oliveira, J.P.M. (eds.) ER 2019. LNCS, vol. 11788, pp. 352–360. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33223-5_29

    Chapter  Google Scholar 

  8. Bork, D., et al.: Requirements engineering for model-based enterprise architecture management with ArchiMate. In: Pergl, R., Babkin, E., Lock, R., Malyzhenkov, P., Merunka, V. (eds.) EOMAS 2018. LNBIP, vol. 332, pp. 16–30. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00787-4_2

    Chapter  Google Scholar 

  9. Bork, D., Karagiannis, D., Pittl, B.: A survey of modeling language specification techniques. Inf. Syst. 87, 101425 (2020). https://doi.org/10.1016/j.is.2019.101425

  10. Buckl, S., Matthes, F., Schweda, C.M.: Classifying enterprise architecture analysis approaches. In: Poler, R., van Sinderen, M., Sanchis, R. (eds.) IWEI 2009. LNBIP, vol. 38, pp. 66–79. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04750-3_6

    Chapter  Google Scholar 

  11. Burgueño, L., Kessentini, M., Wimmer, M., Zschaler, S.: 3rd workshop on artificial intelligence and model-driven engineering. In: International Conference on Model Driven Engineering Languages and Systems Companion, pp. 148–149 (2021)

    Google Scholar 

  12. Buschle, M., Holm, H., Sommestad, T., Ekstedt, M., Shahzad, K.: A tool for automatic enterprise architecture modeling. In: Nurcan, S. (ed.) CAiSE Forum 2011. LNBIP, vol. 107, pp. 1–15. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29749-6_1

    Chapter  Google Scholar 

  13. Buschle, M., Johnson, P., Shahzad, K.: The enterprise architecture analysis tool - support for the predictive, probabilistic architecture modeling framework, pp. 3350–3364 (2013)

    Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Chen, X., Jia, S., Xiang, Y.: A review: knowledge reasoning over knowledge graph. Expert Syst. Appl. 141, 112948 (2020)

    Google Scholar 

  16. Daniel, G., Sunyé, G., Cabot, J.: UMLtoGraphDB: mapping conceptual schemas to graph databases. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 430–444. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46397-1_33

    Chapter  Google Scholar 

  17. Dehmer, M., Emmert-Streib, F., Shi, Y.: Quantitative graph theory: a new branch of graph theory and network science. Inf. Sci. 418–419, 575–580 (2017)

    Article  Google Scholar 

  18. Di Rocco, J., Di Sipio, C., Di Ruscio, D., Nguyen, P.T.: A GNN-based recommender system to assist the specification of metamodels and models. In: International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 70–81 (2021)

    Google Scholar 

  19. Florez, H., Sánchez, M., Villalobos, J.: A catalog of automated analysis methods for enterprise models. Springerplus 5(1), 1–24 (2016). https://doi.org/10.1186/s40064-016-2032-9

    Article  Google Scholar 

  20. Fowler, M.: Refactoring: Improving the Design of Existing Code. Addison-Wesley Professional, Boston (2018)

    Google Scholar 

  21. Frank, U., Strecker, S., Fettke, P., vom Brocke, J., Becker, J., Sinz, E.J.: The research field “modeling business information systems’’ - current challenges and elements of a future research agenda. Bus. Inf. Syst. Eng. 6(1), 39–43 (2014)

    Article  Google Scholar 

  22. Franke, U., Holschke, O., Buschle, M., Narman, P., Rake-Revelant, J.: It consolidation: an optimization approach. In: International Enterprise Distributed Object Computing Conference Workshops, pp. 21–26 (2010)

    Google Scholar 

  23. Giakoumakis, V., Krob, D., Liberti, L., Roda, F.: Technological architecture evolutions of information systems: trade-off and optimization. Concurr. Eng. 20(2), 127–147 (2012)

    Article  Google Scholar 

  24. Glaser, P.L., Ali, S.J., Sallinger, E., Bork, D.: Exploring enterprise architecture knowledge graphs in Archi: the EAKG toolkit (2022). Under review

    Google Scholar 

  25. Hacks, S., Höfert, H., Salentin, J., Yeong, Y.C., Lichter, H.: Towards the definition of enterprise architecture debts. In: 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW), pp. 9–16. IEEE (2019)

    Google Scholar 

  26. Hacks, S., Lichter, H.: A probabilistic enterprise architecture model evolution. In: International Enterprise Distributed Object Computing Conference, pp. 51–57 (2018)

    Google Scholar 

  27. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)

    Article  Google Scholar 

  28. Höfferer, P.: Achieving business process model interoperability using metamodels and ontologies. In: Österle, H., Schelp, J., Winter, R. (eds.) European Conference on Information Systems, ECIS 2007, pp. 1620–1631 (2007)

    Google Scholar 

  29. Holschke, O., Närman, P., Flores, W.R., Eriksson, E., Schönherr, M.: Using enterprise architecture models and Bayesian belief networks for failure impact analysis. In: Feuerlicht, G., Lamersdorf, W. (eds.) ICSOC 2008. LNCS, vol. 5472, pp. 339–350. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01247-1_35

    Chapter  Google Scholar 

  30. Jonkers, H., Band, I., Quartel, D.: The ArchiSurance case study. The Open Group, pp. 1–32 (2012)

    Google Scholar 

  31. Karagiannis, D., Buchmann, R.A.: Linked open models: extending linked open data with conceptual model information. Inf. Syst. 56, 174–197 (2016)

    Article  Google Scholar 

  32. Lankhorst, M.M.: Enterprise Architecture at Work - Modelling, Communication and Analysis. The Enterprise Engineering Series, 2nd edn. Springer, Heidelberg (2009)

    Google Scholar 

  33. Lantow, B., Jugel, D., Wißotzki, M., Lehmann, B., Zimmermann, O., Sandkuhl, K.: Towards a classification framework for approaches to enterprise architecture analysis. In: Horkoff, J., Jeusfeld, M.A., Persson, A. (eds.) PoEM 2016. LNBIP, vol. 267, pp. 335–343. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48393-1_25

    Chapter  Google Scholar 

  34. Maass, W., Storey, V.C.: Pairing conceptual modeling with machine learning. Data Knowl. Eng. 134, 101909 (2021)

    Google Scholar 

  35. Maccormack, A.D., Lagerstrom, R., Baldwin, C.Y.: A methodology for operationalizing enterprise architecture and evaluating enterprise it flexibility. Harvard Business School Working Paper Series# 15-060 (2015)

    Google Scholar 

  36. Medvedev, D., Shani, U., Dori, D.: Gaining insights into conceptual models: a graph-theoretic querying approach. Appl. Sci. 11(2), 765 (2021)

    Article  Google Scholar 

  37. Naranjo, D., Sánchez, M., Villalobos, J.: PRIMROSe: a graph-based approach for enterprise architecture analysis. In: Cordeiro, J., Hammoudi, S., Maciaszek, L., Camp, O., Filipe, J. (eds.) ICEIS 2014. LNBIP, vol. 227, pp. 434–452. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22348-3_24

    Chapter  Google Scholar 

  38. Närman, P., Buschle, M., Ekstedt, M.: An enterprise architecture framework for multi-attribute information systems analysis. Softw. Syst. Model. 13(3), 1085–1116 (2012). https://doi.org/10.1007/s10270-012-0288-2

    Article  Google Scholar 

  39. OMG: ArchiMate® 3.1 Specification. The Open Group (2019). https://pubs.opengroup.org/architecture/archimate3-doc/

  40. Pan, J.Z., Vetere, G., Gómez-Pérez, J.M., Wu, H. (eds.): Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-45654-6

    Book  Google Scholar 

  41. Pittl, B., Bork, D.: Modeling digital enterprise ecosystems with ArchiMate: a mobility provision case study. In: ICServ 2017. LNCS, vol. 10371, pp. 178–189. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61240-9_17

    Chapter  Google Scholar 

  42. Pittl, B., Fill, H.: Transforming enterprise models to linked data via semantic annotations. In: Schaefer, I., Karagiannis, D., Vogelsang, A., Méndez, D., Seidl, C. (eds.) Modellierung 2018. LNI, pp. 55–70. Gesellschaft für Informatik (2018)

    Google Scholar 

  43. Plataniotis, G., de Kinderen, S., Proper, H.A.: Relating decisions in enterprise architecture using decision design graphs. In: 2013 17th IEEE International Enterprise Distributed Object Computing Conference, pp. 139–146. IEEE (2013)

    Google Scholar 

  44. Reimer, U., Bork, D., Fettke, P., Tropmann-Frick, M.: Preface of the first workshop models in AI. In: Companion Proceedings of Modellierung 2020 Short, Workshop and Tools & Demo Papers, pp. 128–129. CEUR Workshop Proceedings (2020)

    Google Scholar 

  45. Salentin, J., Hacks, S.: Towards a catalog of enterprise architecture smells. In: Gronau, N., Heine, M., Krasnova, H., Poustcchi, K. (eds.) Internationalen Tagung Wirtschaftsinformatik, Community Tracks, pp. 276–290. GITO Verlag (2020)

    Google Scholar 

  46. Santana, A., Fischbach, K., de Moura, H.P.: Enterprise architecture analysis and network thinking: a literature review. In: Bui, T.X., Jr., R.H.S. (eds.) 49th Hawaii International Conference on System Sciences, pp. 4566–4575. IEEE (2016)

    Google Scholar 

  47. 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 EDOC Conference, pp. 1–10. IEEE (2016)

    Google Scholar 

  48. Simsek, U., et al.: Knowledge graph lifecycle: building and maintaining knowledge graphs (2021)

    Google Scholar 

  49. Smajevic, M., Bork, D.: From conceptual models to knowledge graphs: a generic model transformation platform. In: International Conference on Model Driven Engineering Languages and Systems Companion, pp. 610–614 (2021)

    Google Scholar 

  50. Smajevic, M., Bork, D.: Towards graph-based analysis of enterprise architecture models. In: Ghose, A., Horkoff, J., Silva Souza, V.E., Parsons, J., Evermann, J. (eds.) ER 2021. LNCS, vol. 13011, pp. 199–209. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89022-3_17

    Chapter  Google Scholar 

  51. 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

    Chapter  Google Scholar 

  52. Tong, Q., Zhang, F., Cheng, J.: Construction of RDF (S) from UML class diagrams. J. Comput. Inf. Technol. 22(4), 237–250 (2014)

    Article  Google Scholar 

  53. Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)

    Article  Google Scholar 

  54. Zou, X.: A survey on application of knowledge graph. In: Journal of Physics: Conference Series, vol. 1487, p. 012016. IOP Publishing (2020)

    Google Scholar 

Download references

Acknowledgements

This work has been partially funded through the Erasmus+ KA220-HED project “Digital Platform Enterprise” (DEMO) with the project number: 2021-1-RO01-KA220-HED-000027576, the project “Enterprise Architecture Knowledge Graphs" funded by a Career Grant of TU Wien, and the Austrian Research Promotion Agency (FFG) via the Austrian Competence Center for Digital Production (CDP) under the contract number 854187.

Author information

Authors and Affiliations

  1. Business Informatics Group, TU Wien, Vienna, Austria

    Philipp-Lorenz Glaser, Syed Juned Ali & Dominik Bork

  2. Database and Artificial Intelligence Group, TU Wien, Vienna, Austria

    Emanuel Sallinger

Authors
  1. Philipp-Lorenz Glaser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Syed Juned Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emanuel Sallinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dominik Bork
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dominik Bork .

Editor information

Editors and Affiliations

  1. Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil

    João Paulo A. Almeida

  2. University of Groningen, Groningen, The Netherlands

    Dimka Karastoyanova

  3. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

  4. Free University of Bozen-Bolzano, Bolzano, Italy

    Marco Montali

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Fabrizio Maria Maggi

  6. University of Twente, Enschede, The Netherlands

    Claudenir M. Fonseca

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Glaser, PL., Ali, S.J., Sallinger, E., Bork, D. (2022). Model-Based Construction of Enterprise Architecture Knowledge Graphs. In: Almeida, J.P.A., Karastoyanova, D., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds) Enterprise Design, Operations, and Computing. EDOC 2022. Lecture Notes in Computer Science, vol 13585. Springer, Cham. https://doi.org/10.1007/978-3-031-17604-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17604-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17603-6

  • Online ISBN: 978-3-031-17604-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation