Abstract
This paper reports on progresses in the ECListener project. ECListener is a project lead by ENEA aimed at a totally automated monitoring of people utterances in the domain of Energy Communities. The project is nested with in the Italian research program for the Electric system supported by the Ministry of Economical Development. The project targets two main sources of information: the news written by professional journalists or occasional contributors and the twits on tweeter. Among several outcomes, one of the most important is the automatic recognition of events related to the Energy Communities: workshops, new communities creations, new laws and enforced regulations.
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References
Cantiello, P., Di Martino, B., Mastroianni, M., Cante, L.C., Graziano, M.: Towards a cloud model choice evaluation: comparison between cost/features and ontology-based analysis. Int. J. Grid Util. Comput. (2022)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Di Martino, B., et al.: A big data pipeline and machine learning for a uniform semantic representation of structured data and documents from information systems of Italian ministry of justice. Int. J. Grid High Perform. Comput. (IJGHPC) (2021)
Di Martino, B., Branco, D., Cante, L.C., Venticinque, S., Scholten, R., Bosma, B.: Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers. J. Ambient Intell. Human. Comput. 1–23 (2021)
Di Martino, B., Cascone, D., Colucci Cante, L., Esposito, A.: Semantic representation and rule based patterns discovery and verification in eProcurement business processes for eGovernment. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 667–676. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_67
Di Martino, B., Colucci Cante, L., Graziano, M., Enrich Sard, R.: Tweets analysis with big data technology and machine learning to evaluate smart and sustainable urban mobility actions in Barcelona. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds.) CISIS 2020. AISC, vol. 1194, pp. 510–519. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50454-0_53
Di Martino, B., Esposito, A., Cante, L.C., Multi agents simulation of justice trials to support control management and reduction of civil trials duration. J. Ambient Intell. Human. Comput. 1–13 (2021)
Di Martino, B., Graziano, M., Colucci Cante, L., Cascone, D.: Analysis of techniques for mapping convolutional neural networks onto cloud edge architectures using SplitFed learning method. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 451, pp. 163–173. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99619-2_16
D’Agostino, G., Tofani, A.: Obserbot: a totally automated watcher to monitor essential services. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 227, pp. 149–158. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75078-7_16
D’Agostino, G., Tofani, A., Di Martino, B., Marulli, F.: Toward ECListener: an unsurpervised intelligent system to monitor energy communities. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 616–626. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_62
Lamy, J.-B.: Owlready: ontology-oriented programming in python with automatic classification and high level constructs for biomedical ontologies. Artif. Intell. Med. 80, 11–28 (2017)
Liang, T.-P., Yang, Y.-F., Chen, D.-N., Yi-Cheng, K.: A semantic-expansion approach to personalized knowledge recommendation. Decis. Support Syst. 45(3), 401–412 (2008)
Di Martino, B., Colucci Cante, L., Venticinque, S.: An ontology framework for evaluating e-mobility innovation. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds.) CISIS 2020. AISC, vol. 1194, pp. 520–529. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50454-0_54
Di Martino, B., Graziano, M.: Semantic techniques for discovering architectural patterns in building information models. Int. J. Comput. Sci. Eng. 24(2), 200–213 (2021)
Di Martino, B., Graziano, M., Cerullo, N.: Semantic techniques for automated recognition of building types in cultural heritage domain. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 657–666. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_66
Di Martino, B., Marulli, F., Graziano, M., Lupi, P.: PrettyTags: an open-source tool for easy and customizable textual multilevel semantic annotations. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 636–645. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_64
Paukkeri, M.-S., García-Plaza, A.P., Pessala, S., Honkela, T.: Learning taxonomic relations from a set of text documents. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 105–112 (2010)
D’Agostino, G., Stanley, H.E., Miyazima, S., Rêgo, H.H.A., Braunstein, L.A.: When a text is translated does the complexity of its vocabulary change? Translations and target readerships. PLoS ONE 9(10), e110213 (2014)
Acknowledgements
The ECListener project is hosted at ENEA, however it benefits of several collaborations among which is worth mentioning the University of Rome I “Sapienza”. Ongoing activities for the web crawling are also performed in collaboration with ENEA computational center CRESCO center in Portici. This work was supported by the Project 1.7 T́echnologies for the efficient penetration of the electric vector in the final use “ within the Èlectrical System Research” Programme Agreements 19–21 between ENEA and the Ministry of Economic Development. The authors wish to thank all the ENEA team for helpful suggestions in the Energy Community Ontology definition, and Emiliano Casalicchio (Univ. Roma I “Sapienza”) and Giuseppe Santomauro (ENEA-TERIN/ICT) who are actively collaborating in the further developments of the project. Special thanks are due to Luigi La Porta for his priceless contribution in the platform development and management. Useful discussions with Alberto Botti and Gabriele Di Segni of the Arakne company are kindly acknowledged. The group of experts of the Smart Energy Division is also aknowledged: Mauro Annunziato, Sabrina Romano, Claudia Meloni, Piero De Sabata, Gianluca D’Agosta, Stefano Pizzuti.
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D’Agostino, G. et al. (2022). ECListener: A Platform for Monitoring Energy Communities. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2022. Lecture Notes in Networks and Systems, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-08812-4_48
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