Toward automatic generation of linguistic advice for saving energy at home
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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.
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