Toward automatic generation of linguistic advice for saving energy at home
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.
KeywordsNatural 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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Mencar C, Fanelli A (2008) Interpretability constraints for fuzzy information granulation. Inf Sci 178(24):4585–4618Google Scholar
- NatConsumers European project: (2015). http://www.natconsumers.eu. Accessed 19 Oct 2016
- 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
- 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
- van Deemter K (2016) Computational models of referring: a study in cognitive science. MIT Press, CambridgeGoogle Scholar
- Yager RR (1995) Fuzzy summaries in database mining. In: Proceedings of the 11th conference on artificial intelligent for applications, pp 265–269Google Scholar