Analysis of Knowledge-Intensive Processes Focused on the Communication Perspective

  • Pedro Henrique Piccoli Richetti
  • João Carlos de A.R. GonçalvesEmail author
  • Fernanda Araujo Baião
  • Flávia Maria Santoro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)


Knowledge-intensive Processes (KiPs) are unstructured processes that demand an understanding beyond control flow and data. Being knowledge-centric and varying at each instance, KiPs demand new perspectives for proper process analysis. Most KiPs have strong collaboration characteristics, where interactions among participants are crucial to achieve process goals. Process participants perform activities and collaborate with each other, driven by their Beliefs, Desires and Intentions; therefore, the analysis of these elements is vital to the correct understanding, modeling and execution of a KiP. This research proposes a method based on Speech Act Theory and Process Mining to discover the flow of speech acts related to Beliefs, Desires and Intentions from event logs, and shows how this relation fosters process performance analysis. The approach was evaluated through a case study in a real life scenario, and results showed that relevant insights in forms of speech acts flow patterns were discovered and related to performance issues of the KiP.


Knowledge-intensive Process Speech act Process performance measures 


  1. 1.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011).
  2. 2.
    Austin, J.L.: How to Do Things with Words. Oxford University Press, Oxford (1975)CrossRefGoogle Scholar
  3. 3.
    Bach, K., Harnish, R.: Linguistic Communication and Speech Acts. MIT Press, Cambridge (1979)Google Scholar
  4. 4.
    de Carvalho, V.R., Cohen, W.W.: On the collective classification of email “speech acts". In: SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil, 15–19 August 2005, pp. 345–352 (2005).
  5. 5.
    Cohen, W.W., Carvalho, V.R., Mitchell, T.M.: Learning to classify email into speech acts. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, Barcelona, Spain, pp. 309–316 (2004)Google Scholar
  6. 6.
    Cunningham, H., Maynard, D., Bontcheva, K.: Text Processing with Gate. Gateway Press CA, Murphys (2011)Google Scholar
  7. 7.
    del-Río-Ortega, A., Resinas, M., Ruiz-Cortés, A.: Defining process performance indicators: an ontological approach. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 555–572. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16934-2_41CrossRefGoogle Scholar
  8. 8.
    Di Ciccio, C., Marrella, A., Russo, A.: Knowledge-intensive processes: An overview of contemporary approaches. In: Proceedings of the 1st International Workshop on Knowledge-intensive Business Processes, KiBP@KR 2012, Rome, Italy, 15 June 2012, pp. 33–47 (2012)Google Scholar
  9. 9.
    Greenwood, M.A., Tablan, V., Maynard, D.: Gate mımir: Answering questions google can’t. In: Proceedings of the 10th International Semantic Web Conference (ISWC2011), pp. 466–471 (2011)Google Scholar
  10. 10.
    Isik, Ö., Mertens, W., den Bergh, J.V.: Practices of knowledge intensive process management quantitative insights. Bus. Proc. Manag. J. 19(3), 515–534 (2013). Scholar
  11. 11.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Process and deviation exploration with inductive visual miner. In: Proceedings of the BPM Demo Sessions 2014 Co-located with the 12th International Conference on Business Process Management (BPM 2014), The Netherlands, 10 September 2014, p. 46 (2014)Google Scholar
  12. 12.
    Mannhardt, F., de Leoni, M., Reijers, H.A.: The multi-perspective process explorer. In: Proceedings of the BPM Demo Session 2015 Co-located with the 13th International Conference on Business Process Management (BPM 2015), Innsbruck, Austria, 2 September 2015, pp. 130–134 (2015)Google Scholar
  13. 13.
    Mavaddat, M.: Business process discovery through conversation log analysis in pluralist and coercive problem contexts. Ph.D. thesis, University of the West of England (2013)Google Scholar
  14. 14.
    Mavaddat, M., Beeson, I., Green, S., Sa, J.: Facilitating business process discovery using email analysis. In: The First International Conference on Business Intelligence and Technology. Citeseer (2011)Google Scholar
  15. 15.
    McChesney, C., Covey, S., Huling, J.: The 4 Disciplines of Execution: Achieving Your Wildly Important Goals. Simon and Schuster, New York (2012)Google Scholar
  16. 16.
    Morales-Ramirez, I., Perini, A.: Discovering speech acts in online discussions: A tool-supported method. In: Joint Proceedings of the CAiSE 2014 Forum and CAiSE 2014 Doctoral Consortium co-located, Thessaloniki, Greece, 18–20 June 2014, pp. 137–144 (2014)Google Scholar
  17. 17.
    Rus, V., Graesser, A.C., Moldovan, C., Niraula, N.B.: Automatic discovery of speech act categories in educational games. In: Proceedings of the 5th International Conference on Educational Data Mining, Chania, Greece, 19–21 June 2012, pp. 25–32 (2012)Google Scholar
  18. 18.
    dos Santos França, J.B., Netto, J.M., do E. Santo Carvalho, J., Santoro, F.M., Baião, F.A., Pimentel, M.: KIPO: The knowledge-intensive process ontology. Softw. Syst. Model. 14(3), 1127–1157 (2015)CrossRefGoogle Scholar
  19. 19.
    Searle, J.R.: A Taxonomy of Illocutionary Acts. Linguistic Agency University of Trier (1976)Google Scholar
  20. 20.
    Searle, J.R., Vanderveken, D.: Foundations of Illocutionary Logic. CUP Archive (1985)Google Scholar
  21. 21.
    Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R.A., Jurafsky, D., Taylor, P., Martin, R., Ess-Dykema, C.V., Meteer, M.: Dialogue act modeling for automatic tagging and recognition of conversational speech. CoRR cs.CL/0006023 (2000)CrossRefGoogle Scholar
  22. 22.
    Tenschert, J., Lenz, R.: Towards speech-act-based adaptive case management. In: 20th IEEE International Enterprise Distributed Object Computing Workshop, EDOC Workshops 2016, Vienna, Austria, 5–9 September 2016, pp. 1–8 (2016).
  23. 23.
    Van Der Aalst, W., Adriansyah, A., De Medeiros, A.K.A., Arcieri, F., Baier, T., Blickle, T., Bose, J.C., van den Brand, P., Brandtjen, R., Buijs, J., et al.: Process mining manifesto. In: International Conference on Business Process Management, pp. 169–194. Springer, Heidelberg (2011)Google Scholar
  24. 24.
    Vanderveeken, D.: Meaning and Speech Acts: Principles of Language Use. Cambridge University Press, Cambridge (1990)Google Scholar
  25. 25.
    Verbeek, H.M.W., Buijs, J.C.A.M., Dongen, B.F., Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-17722-4_5CrossRefGoogle Scholar
  26. 26.
    Vosoughi, S., Roy, D.: Tweet acts: A speech act classifier for twitter. In: Proceedings of the Tenth International Conference on Web and Social Media, Cologne, Germany, 17–20 May 2016, pp. 711–715 (2016)Google Scholar
  27. 27.
    Wang, G.A., Wang, H.J., Li, J., Abrahams, A.S., Fan, W.: An analytical framework for understanding knowledge-sharing processes in online Q&A communities. ACM Trans. Manage. Inf. Syst. 5(4), 18:1–18:31 (2015)Google Scholar
  28. 28.
    Zhang, R., Li, W., Gao, D., You, O.: Automatic twitter topic summarization with speech acts. IEEE Trans. Audio Speech Lang. Process. 21(3), 649–658 (2013). Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pedro Henrique Piccoli Richetti
    • 1
  • João Carlos de A.R. Gonçalves
    • 1
    Email author
  • Fernanda Araujo Baião
    • 1
  • Flávia Maria Santoro
    • 1
  1. 1.Department of Applied InformaticsFederal University of the State of Rio de JaneiroRio de JaneiroBrazil

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