Soft Computing

, Volume 22, Issue 2, pp 345–359 | Cite as

Toward automatic generation of linguistic advice for saving energy at home

  • Patricia Conde-ClementeEmail author
  • Jose M. Alonso
  • Gracian Trivino


The increased demand of systems able to generate reports in natural language from numerical data involves the search for new solutions. This paper presents an adaptation of standard natural language generation methodologies to generate customized linguistic descriptions of data. Namely, we merge one of the most well-known architectures in the natural language generation research field together with our previous architecture for generating linguistic descriptions of complex phenomena. The latter is supported by the computational theory of perceptions which comes from the fuzzy sets and systems research field. We include a practical case of use dealing with the problem of inefficient consumption of energy at households. It generates natural language recommendations adapted to each household to promote a more responsible consumption. The proposal reveals opportunities of collaboration between the different research communities that are involved.


Natural computing Data science Linguistic description of data Computing with perceptions Computational intelligence Fuzzy sets and systems 



We thank NatConsumers partners project for their help in performing this research. We thank especially our Hungarian partners Ariosz ltd. that have provided us with the database and the taxonomy of consumers that we have used in this work. NatConsumers project is funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 657672. This research was also partially funded by the Spanish Ministry of Science and Innovation under Grant FPI-MICINN BES-2012-057427 and the Spanish Ministry of Economy and Competitiveness under projects TIN2014-56633-C3-1-R, TIN2014-56633-C3-3-R and TIN2014-56967-R.

Compliance with ethical standards

Conflict of interest

All authors (Patricia Conde-Clemente, Jose M. Alonso and Gracian Trivino) declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Alcalá R, Nojima Y, Herrera F, Ishibuchi H (2011) Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions. Soft Comput 15(12):2303–2318CrossRefGoogle Scholar
  2. Alonso JM, Castiello C, Mencar C (2015) Interpretability of fuzzy systems: current research trends and prospects. In: Kacprzyk J, Pedrycz W (eds) Springer handbook of computational intelligence. Springer, Heidelberg, pp 219–237CrossRefGoogle Scholar
  3. Alvarez-Alvarez A, Trivino G (2013) Linguistic description of the human gait quality. Eng Appl Artif Intell 26(1):13–23CrossRefGoogle Scholar
  4. Arguelles L, Trivino G (2013) I-struve: automatic linguistic descriptions of visual double stars. Eng Appl Artif Intell 26(9):2083–2092CrossRefGoogle Scholar
  5. Busemann S, Horacek H (1997) Generating air quality reports from environmental data. In: Proceedings of DFKI workshop on natural language generation, pp 15–21Google Scholar
  6. Cambria E, White B (2014) Jumping NLP curves: a review of natural language processing research. IEEE Comput Intell Mag 9(2):48–57CrossRefGoogle Scholar
  7. Castillo-Ortega R, Marín N, Sánchez D (2010) Time series comparison using linguistic fuzzy techniques. Proceedings of the 13th international conference on information processing and management uncertainty. Springer, Berlin, pp 330–339Google Scholar
  8. Castillo-Ortega R, Marín N, Sánchez D (2011) A fuzzy approach to the linguistic summarization of time series. J Mult Valued Log Soft Comput 17(2–3):157–182Google Scholar
  9. Castillo-Ortega R, Marín N, Sánchez D (2011) Linguistic query answering on data cubes with time dimension. Int J Intell Syst 26(10):1002–1021CrossRefGoogle Scholar
  10. Coch J (1998) Interactive generation and knowledge administration in MultiMeteo. In: Proceedings of the 9th international workshop on natural language generation, pp 300–303Google Scholar
  11. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases, vol. 19. Advances in fuzzy systems—applications and theory World Scientific Publishing, SingaporeGoogle Scholar
  12. Delgado M, Ruiz MD, Sánchez D, Vila MA (2014) Fuzzy quantification: a state of the art. Fuzzy Sets Syst 242:1–30MathSciNetCrossRefzbMATHGoogle Scholar
  13. Dhar V (2013) Data science and prediction. Commun ACM 56(12):64–73CrossRefGoogle Scholar
  14. de Oliveira JV (1999) Semantic constraints for membership function optimization. IEEE Trans Syst Man Cybern A 29(1):128–138MathSciNetCrossRefGoogle Scholar
  15. Gatt A, Marín N, Portet F, Sánchez D (2016) The role of graduality for referring expression generation in visual scenes. Springer International Publishing, Cham, pp 191–203Google Scholar
  16. Gatt A, Portet F (2016) Multilingual generation of uncertain temporal expressions from data: a study of a possibilistic formalism and its consistency with human subjective evaluations. Fuzzy Sets Syst 285:73–93MathSciNetCrossRefGoogle Scholar
  17. Goldberg E, Driedger N, Kittredge RI (1994) Using natural-language processing to produce weather forecasts. IEEE Expert 9(2):45–53CrossRefGoogle Scholar
  18. Kacprzyk J, Wilbik A, Zadrozny S (2008) Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets Syst 159(12):1485–1499MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kacprzyk J, Yager RR (2001) Linguistic summaries of data using fuzzy logic. Int J Gen Syst 30:133–154MathSciNetCrossRefzbMATHGoogle Scholar
  20. Kacprzyk J, Yager R, Zadrożny S (2000) A fuzzy logic based approach to linguistic summaries of databases. Int J Appl Math Comput Sci 10:813–834zbMATHGoogle Scholar
  21. Kacprzyk J, Zadrożny S (2010) Computing with words is an implementable paradigm: fuzzy queries, linguistic data summaries and natural language generation. IEEE Trans Fuzzy Syst 18(3):461–472CrossRefGoogle Scholar
  22. Kittredge R, Polguére A, Goldberg E (1986) Synthesizing weather forecasts from formated data. In: Proceedings of the 11th conference on computational linguistics, pp 563–565Google Scholar
  23. Losada DE, Díaz-Hermida F, Bugarín A, Barro S (2004) Experiments on using fuzzy quantified sentences in adhoc retrieval. In: Proceedings of the ACM symposium applied computing, pp 1059–1064Google Scholar
  24. Marín N, Sánchez D (2016) On generating linguistic descriptions of time series. Fuzzy Sets Syst 285:6–30MathSciNetCrossRefGoogle Scholar
  25. Mencar C, Fanelli A (2008) Interpretability constraints for fuzzy information granulation. Inf Sci 178(24):4585–4618Google Scholar
  26. Menendez C, Eciolaza L, Trivino G (2014) Generating advices with emotional content for promoting efficient consumption of energy. Int J Uncertain Fuzziness Knowl Based Syst 22(5):677–697CrossRefGoogle Scholar
  27. NatConsumers European project: (2015). Accessed 19 Oct 2016
  28. Portet F, Reiter E, Gatt A, Hunter J, Sripada S, Freer Y, Sykes C (2009) Automatic generation of textual summaries from neonatal intensive care data. Artif Intell 173(7–8):789–816CrossRefGoogle Scholar
  29. Ramos-Soto A, Bugarín A, Barro S (2016) On the role of linguistic descriptions of data in the building of natural language generation systems. Fuzzy Sets Syst 285:31–51MathSciNetCrossRefGoogle Scholar
  30. Ramos-Soto A, Bugarín A, Barro S, Díaz-Hermida F (2013) Automatic linguistic descriptions of meteorological data. A soft computing approach for converting open data to open information. In: Proceedings of the 8th Iberian conference on information systems and technologies (CISTI)Google Scholar
  31. Ramos-Soto A, Bugarín AJ, Barro S, Taboada J (2015) Linguistic descriptions for automatic generation of textual short-term weather forecasts on real prediction data. IEEE Trans Fuzzy Syst 23(1):44–57CrossRefGoogle Scholar
  32. Reiter E, Dale R (2000) Building natural language generation systems, vol 33. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  33. Sanchez-Valdes D, Alvarez-Alvarez A, Trivino G (2016) Dynamic linguistic descriptions of time series applied to self-track the physical activity. Fuzzy Sets Syst 285:162–181MathSciNetCrossRefGoogle Scholar
  34. Trivino G, Sanchez-Valdes D (2015) Generation of linguistic advices for saving energy: architecture. In: Proceedings of the 4th international conference theory and practice of natural computing, pp 83–94Google Scholar
  35. Trivino G, Sugeno M (2013) Towards linguistic descriptions of phenomena. Int J Approx Reason 54(1):22–34CrossRefGoogle Scholar
  36. van Deemter K (2009) Utility and language generation: the case of vagueness. J Philos Log 38(6):607–632MathSciNetCrossRefzbMATHGoogle Scholar
  37. van Deemter K (2016) Computational models of referring: a study in cognitive science. MIT Press, CambridgeGoogle Scholar
  38. Yager RR (1982) A new approach to the summarization of data. Inf Sci 28:69–86MathSciNetCrossRefzbMATHGoogle Scholar
  39. Yager RR (1995) Fuzzy summaries in database mining. In: Proceedings of the 11th conference on artificial intelligent for applications, pp 265–269Google Scholar
  40. Zadeh LA (1983) A computational approach to fuzzy quantifiers in natural languages. Comput Math Appl 9:149–184MathSciNetCrossRefzbMATHGoogle Scholar
  41. Zadeh LA (1996) Fuzzy sets and information granularity. In: Klir GJ, Yuan B (eds) Fuzzy sets, fuzzy logic, and fuzzy systems. World Scientific Publishing Co., Inc, River Edge, NJ, pp 433–448CrossRefGoogle Scholar
  42. Zadeh LA (1999) From computing with numbers to computing with words–from manipulation of measurements to manipulation of perceptions. IEEE Trans Circuits Syst I 45(1):105–119MathSciNetCrossRefzbMATHGoogle Scholar
  43. Zadeh LA (2002) Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. J Stat Plann Inference 105:233–264MathSciNetCrossRefzbMATHGoogle Scholar
  44. Zadeh LA (1994) Soft computing and fuzzy logic. IEEE Softw 11(6):48–56CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Departamento de InformáticaUniversidad of OviedoGijónSpain
  2. 2.Centro Singular de Investigacion en Tecnoloxias da Informacion (CiTIUS)Universidade de Santiago de CompostelaSantiago de CompostelaSpain
  3. 3.Phedes LabGijónSpain

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