Journal of Intelligent Information Systems

, Volume 53, Issue 3, pp 409–430 | Cite as

A grammar inference approach for language self-adaptation and evolution in digital ecosystems

  • Fernando Ferri
  • Arianna D’UliziaEmail author
  • Patrizia Grifoni


Socialization is the essential building process of any society in natural ecosystems. Effective socialization processes have been investigated for both “biotic” (human) and “abiotic” (virtual) entities, also within digital ecosystems in the perspective of common and self-adaptive languages. In this paper, we propose an approach for socialization, language self-adaptation, and evolution that enables an effective communicative interaction among digital entities acting in a digital ecosystem. The proposed method relies on an adaptable and extensible grammatical formalism, named Digital Ecosystem Grammar (DEG). This grammar enables digital entities to interpret the messages sent by other entities by using interaction, learning and evolution actions. Moreover, a grammar learning algorithm is applied to provide the self-adaptation mechanisms that allow the digital environment to adapt the interaction language according to new messages. The approach was suitable to support the characteristics of self-adaptation, context-awareness, evolvability, and semanticity of a digital ecosystem language.


Digital ecosystem Interaction Grammar Socialization Language evolution 


Supplementary material

10844_2019_566_MOESM1_ESM.docx (660 kb)
(DOCX 660 KB)
10844_2019_566_MOESM2_ESM.docx (812 kb)
(DOCX 811 KB)


  1. Abeywickrama, D.B., & Ovaska, E. (2017). A survey of autonomic computing methods in digital service ecosystems. Service Oriented Computing and Applications, 11(1), 1–31.Google Scholar
  2. Aguiléra, V., & Tordeux, A. (2014). A new kind of fundamental diagram with an application to road traffic emission modeling. Journal of Advanced Transportation, 48 (2), 165–184.Google Scholar
  3. Atzori, L., Iera, A., Morabito, G. (2014). From “smart objects” to “social objects”: the next evolutionary step of the internet of things. IEEE Communications Magazine, 52(1), 97–105.Google Scholar
  4. Bassil, Y. (2012). Communication language specifications for digital ecosystems. International Journal of Advanced Research in Computer Science 3(1).Google Scholar
  5. Blanke, T. (2014). Digital asset ecosystems: rethinking crowds and cloud. Amsterdam: Elsevier.Google Scholar
  6. Briscoe, G. (2007). Creating a Digital Ecosystem: Service-oriented architectures with distributed evolutionary computing. arXiv:0712.4159.
  7. Caschera, M.C., D’Ulizia, A., Ferri, F., Grifoni, P. (in press). MONDE: a method for predicting social network dynamics and evolution. Evolving systems. Berlin: Springer. Available at: Scholar
  8. Chatterjee, J. (2010). Digital ecosystem for knowledge, learning and exchange: exploring socio-technical concepts and adoption. In Digital ecosystems (pp. 44–61). Berlin: Springer.Google Scholar
  9. Csuhaj-Varjú, E., Dassow, J., Kelemen, J., Paun, G. (1994). Grammar systems: a grammatical approach to distribution and cooperation. London: Gordon and Breach.zbMATHGoogle Scholar
  10. Csuhaj-Varjú, E., Kelemen, J., Kelemenová, A., Paun, G. (1997). Eco-grammar systems: a grammatical framework for studying lifelike interactions. Artificial Life, 3, 1–28.Google Scholar
  11. D’Andrea, A., D’Ulizia, A., Ferri, F., Grifoni, P. (2017). EMAG: an extended multimodal attribute grammar for behavioural features. Digital Scholarship in the Humanities, 32(2), 251–275.Google Scholar
  12. De Tommasi, M., Cisternino, V., Corallo, A. (2005). A rule-based and computation-independent business modelling language for digital business ecosystems. In International conference on knowledge-based and intelligent information and engineering systems (pp. 134–141). Berlin: Springer.Google Scholar
  13. Dini, P. (2007). A scientific foundation for digital ecosystems. In F. Nachira, A. Nicolai, P. Dini, M. LeLouarn, L. Rivera Leon (Eds.), Digital business ecosystems (pp. 24–47). Luxembourg: European Commission.Google Scholar
  14. Dini, P., Munro, A.J., Iqani, M., Zeller, F., Moschoyiannis, S., Gabaldon, J., Nykanen, O. (2008). D1.2–foundations of the theory of associative autopoietic digital ecosystems: Part 1. OPAALS deliverable, European commission.Google Scholar
  15. Dou, E.Y. (2015). UniShuttle - a small-scale intelligent transport system in the connected mobility digital ecosystem, Doctor of Philosophy thesis, School of Information Systems and Technology, University of Wollongong.Google Scholar
  16. D’Ulizia, A., Ferri, F., Grifoni, P., Rafanelli, M. (2006). Relaxing constraints on GeoPQL operators for improving query answering. In 17th international conference on database and expert systems applications (DEXA’06), Lecture Notes in Computer Science 4080 (pp. 728–737): Springer.Google Scholar
  17. D’Ulizia, A., Ferri, F., Grifoni, P. (2007). A hybrid grammar-based approach to multimodal languages specification. In OTM 2007 workshop proceedings, LNCS 4805 (pp. 367–376). Berlin: Springer.Google Scholar
  18. D’Ulizia, A., Ferri, F., Grifoni, P. (2008). Toward the development of an integrative framework for multimodal dialogue processing. In OTM 2008 Workshops Proceedings, LNCS 5333 (pp. 509–518). Berlin: Springer.Google Scholar
  19. D’Ulizia, A., Ferri, F., Formica, A., Grifoni, P. (2009). Approximating geographical queries. Journal of Computer Science and Technology, 24(6), 1109–1124.Google Scholar
  20. D’Ulizia, A., Ferri, F., Grifoni, P. (2010). Generating multimodal grammars for multimodal dialogue processing. IEEE Transactions on Systems, Man and Cybernetics Part A: Systems and Humans, 40(6), 1130–1145.Google Scholar
  21. D’Ulizia, A., Ferri, F., Grifoni, P. (2011). A learning algorithm for multimodal grammar inference. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 41(6), 1495–1510.Google Scholar
  22. Ferri, F., D’Ulizia, A., Grifoni, P. (2012). Multimodal language specification for human adaptive mechatronics. Journal of Next Generation Information Technology (JNIT), 3(1), 47–57.Google Scholar
  23. Ferri, F., Grifoni, P., Caschera, M.C., D’Ulizia, A., Pratico’, C. (2013). KRC: KnowIng crowdsourcing platform supporting creativity and innovation. AISS: Advances in Information Sciences and Service Sciences, 5(16), 1–15.Google Scholar
  24. Ferri, F., Grifoni, P., Caschera, M.C., D’Andrea, A., D’Ulizia, A., Guzzo, T. (2014). An ecosystemic environment for knowledge and services sharing on creative enterprises. In Proceedings of the 6th international conference on management of emergent digital ecosystems (pp. 27–33): ACM.Google Scholar
  25. Ferri, F., D’Ulizia, A., Grifoni, P. (2018). Computational models of language evolution: Challenges and future perspectives. Journal of Universal Computer Science, 24(10), 1345–1377.Google Scholar
  26. Fu, H. (2006). Formal concept analysis for digital ecosystem. In 5th International conference on machine learning and applications (ICMLA’06) (pp. 143–148): IEEE.Google Scholar
  27. Gomez, S., Andersson, H., Park, J., Maw, S., Crook, A., Orsmond, P. (2013). A digital ecosystems model of assessment feedback on student learning. Higher Education Studies, 3(2), 41.Google Scholar
  28. Grifoni, P., Ferri, F., D’Andrea, A., Guzzo, T., Pratico’, C. (2014). SoN-KInG: a digital eco-system for innovation in professional and business domains. Journal of Systems and Information Technology, 16(1), 77–92.Google Scholar
  29. Grifoni, P., D’Ulizia, A., Ferri, F. (2016). Computational methods and grammars in language evolution: a survey. Artificial Intelligence Review, 45(3), 369–403.Google Scholar
  30. Grifoni, P., D’Ulizia, A., Ferri, F. (2018). Context-awareness in location based services in the big data era. In Mobile big data (pp. 85–127). Cham: Springer.Google Scholar
  31. Hall, F.L. (1996). Traffic stream characteristics. Traffic flow theory. US federal highway administration.Google Scholar
  32. Heylighen, F. (2008). Complexity and self-organization. In Bates, M.J., & Maack, M.N. (Eds.) Encyclopedia of library and information sciences. New York: Taylor and Francis.Google Scholar
  33. Jackson, R.L. II. (2010). Encyclopedia of identity Vol. 1. Newbury Park: Sage.Google Scholar
  34. Kephart, J. (2005). Research challenges of autonomic computing. In Proceedings of the 27th international conference on software engineering 2005 (pp. 15–22): IEEE.Google Scholar
  35. Knuth, D.E. (1968). Semantics of context-free languages. Mathematical Systems Theory, 2, 127–145.MathSciNetzbMATHGoogle Scholar
  36. Langley, R.B. (1995). NMEA 0183: a GPS receiver interface standard. GPS World, 6(7), 54–57.Google Scholar
  37. Lapteva, O., Peukert, H., Nykänen, O., Eder, R., Zeller, F. (2009). Models of the evolutionary framework for language. Report to the European commission, RP6 Information-Society-Technology IST-2005-034824.Google Scholar
  38. Li, W., Badr, Y., Biennier, F. (2012). Digital ecosystems: challenges and prospects. In The international conference on management of emergent digital ecosystems (MEDES’12) (p. 117).Google Scholar
  39. Lurgi, M. (2010). An ecologically inspired simulation tool for managing digital ecosystems. In Proceedings of the international conference on management of emergent digital ecosystems (pp. 17–24): ACM.Google Scholar
  40. Macías-Escrivá, F.D., Haber, R., del Toro, R., Hernandez, V. (2013). Self-adaptive systems: a survey of current approaches, research challenges and applications. Expert Systems with Applications, 40(18), 7267–7279.Google Scholar
  41. Manzalini, A., Brgulja, N., Moiso, C., Minerva, R. (2012). Autonomic nature-inspired eco-systems. In Gavrilova, M., Tan, C.J.K., Phan, C. (Eds.) Transactions on computational scienceXV (pp. 158–191). Berlin: Springer.Google Scholar
  42. Mitchell, P.M., Santorini, B., Marcinkiewicz, M.A. (1994). Building a large annotated corpus of english: the penn treebank. Computational Linguistics, 19(2), 313–330.Google Scholar
  43. Nachira, F., Nicolai, A., Dini, P., Le Louarn, M., Leon, L.R. (2007). Digital business ecosystems. European Commission Information Society and Media.Google Scholar
  44. Palmonari, M., & Batini, C. (2009). Abstract ER IA: a web language for conceptual metadata integration and abstraction in the large. In Proceedings of the international conference on management of emergent digital ecosystems (p. 18): ACM.Google Scholar
  45. Petasis, G., Paliouras, G., Karkaletsis, V., Halatsis, C. (2004). e-GRIDS: computationally efficient grammatical inference from positive examples. GRAMMARS, 7, 69–110.Google Scholar
  46. Renya, J. (2011). Digital teaching and learning ecosystem (DTLE): a theoretical approach for online learning environments. In Williams, G., Statham, P., Brown, N., Cleland, B. (Eds.) Proceedings ascilite Hobart changing demands, changing directions (pp. 1083–1088).Google Scholar
  47. Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14, 465–471.zbMATHGoogle Scholar
  48. Robertson, D. (2004). A lightweight coordination calculus for agent systems. In Declarative agent languages and technologies II (pp. 183–197). Berlin: Springer.Google Scholar
  49. Schieffelin, B.B., & Ochs, E. (Eds.). (1986). Language socialization across cultures (No. 3). Cambridge: Cambridge University Press.Google Scholar
  50. Serbanati, L.D., Vasilateanu, A., Nita, B. (2013). Strengthening context-awareness of virtual species in digital ecosystems. In 19th international conference on control systems and computer science (pp. 503–510): IEEE.Google Scholar
  51. Toutanova, K., Klein, D., Manning, C., Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of HLT-NAACL (pp. 252–259).Google Scholar
  52. Walker, W., Lamere, Kwok, P., Raj, B., Singh, R., Gouvea, E., Wolf, P., Woelfel, J. (2004). Sphinx-4: A flexible open source framework for speech recognition. Technical Report TR2004-0811, SMLI, Carnegie Mellon University, SUN MICROSYSTEMS INC.Google Scholar
  53. Wang, Y. (2015). Socializing multimodal sensors for information fusion. In Proceedings of the 23rd annual conference on multimedia conference (pp. 653–656): ACM.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Consiglio Nazionale delle Ricerche - IRPPSRomeItaly

Personalised recommendations