An Environment for Collective Perception based on Fuzzy and Semantic Approaches

D’Aniello, Giuseppe; Gaeta, Matteo; Loia, Francesca; Reformat, Marek; Toti, Daniele

doi:10.1515/jaiscr-2018-0013

Abstract

This work proposes a software environment implementing a methodology for acquiring and exploiting the collective perception (CP) of Points of Interests (POIs) in a Smart City, which is meant to support decision makers in urban planning and management. This environment relies upon semantic knowledge discovery techniques and fuzzy computational approaches, including natural language processing, sentiment analysis, POI signatures and Fuzzy Cognitive Maps, turning them into a cohesive architectural blend in order to effectively gather the realistic perception of a user community towards given areas and attractions of a Smart City. The environment has been put to the test via a thorough experimentation against a massive user base of an online community with respect to a large metropolitan city (the City of Naples). Such an experimentation yielded consistent results, useful for providing decision makers with a clear awareness of the positive as well as critical aspects of urban areas, and thus helping them shape the measures to be taken for an improved city management and development.

References

[1] F. Antunes and J. Costa, Integrating decision support and social networks, Advances in Human-Computer Interaction, vol. 2012, no. 574276, 2012.
Search in Google Scholar Back to article
[2] Smarter Cities - New cognitive approaches to long-standing challenges, http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/, accessed: 2017-02-10.
Search in Google Scholar Back to article
[3] D. Doran, K. Severin, S. Gokhale, and A. Dagnino, Social media enabled human sensing for smart cities, AI Communications, vol. 29, no. 1, pp. 57–75, 2016.10.3233/AIC-150683
Search in Google Scholar Back to article
[4] G. P. Hancke, G. P. Hancke Jr et al., The role of advanced sensing in smart cities, Sensors, vol. 13, no. 1, pp. 393–425, 2012.10.3390/s130100393
Search in Google Scholar Back to article
[5] G. R. Ceballos and V. M. Larios, A model to promote citizen driven government in a smart city: Use case at gdl smart city, in 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–6, 2016.10.1109/ISC2.2016.7580873
Search in Google Scholar Back to article
[6] P. Zeile, B. Resch, L. Dörrzapf, J.-P. Exner, G. Sagl, A. Summa, and M. Sudmanns, Urban emotions–tools of integrating people perception into urban planning, in REAL CORP 2015. PLAN TOGETHER–RIGHT NOW–OVERALL. From Vision to Reality for Vibrant Cities and Regions. Proceedings of 20th International Conference on Urban Planning, Regional Development and Information Society. CORP–Competence Center of Urban and Regional Planning, pp. 905–912, 2015.
Search in Google Scholar Back to article
[7] A. Vakali, D. Chatzakou, V. A. Koutsonikola, and G. Andreadis, Social data sentiment analysis in smart environments-extending dual polarities for crowd pulse capturing. in DATA, pp. 175–182, 2013.
Search in Google Scholar Back to article
[8] D. Toti and M. Rinelli, On the road to speed-reading and fast learning with CONCEPTUM, in Proceedings - 2016 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2016, pp. 1–6, 2016.10.1109/INCoS.2016.30
Search in Google Scholar Back to article
[9] S. Baccianella, A. Esuli, and F. Sebastiani, Senti- WordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining, in Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10). Valletta, Malta: European Language Resources Association (ELRA), 2010.
Search in Google Scholar Back to article
[10] G. D’Aniello, A. Gaeta, M. Gaeta, V. Loia, and M. Reformat, Collective awareness in Smart City with Fuzzy Cognitive Maps and Fuzzy sets, in 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016.10.1109/FUZZ-IEEE.2016.7737875
Search in Google Scholar Back to article
[11] R. R. Yager and M. Z. Reformat, Looking for likeminded individuals in social networks using tagging and e fuzzy sets, Fuzzy Systems, IEEE Transactions on, vol. 21, no. 4, pp. 672–687, 2013.10.1109/TFUZZ.2012.2227263
Search in Google Scholar Back to article
[12] B. Kosko, Fuzzy cognitive maps, International journal of man-machine studies, vol. 24, no. 1, pp. 65–75, 1986.10.1016/S0020-7373(86)80040-2
Search in Google Scholar Back to article
[13] G. D’Aniello, V. Loia, and F. Orciuoli, A multiagent fuzzy consensus model in a situation awareness framework, Applied Soft Computing, vol. 30, pp. 430 – 440, 2015.10.1016/j.asoc.2015.01.061
Search in Google Scholar Back to article
[14] M. Olazabal and U. Pascual, Use of fuzzy cognitive maps to study urban resilience and transformation, Environmental Innovation and Societal Transitions, 2015.10.1016/j.eist.2015.06.006
Search in Google Scholar Back to article
[15] U. Özesmi and S. L. Özesmi, Ecological models based on peoples knowledge: a multi-step fuzzy cognitive mapping approach, Ecological Modelling, vol. 176, no. 1, pp. 43–64, 2004.10.1016/j.ecolmodel.2003.10.027
Search in Google Scholar Back to article
[16] F. Habib and A. Shokoohi, Classification and resolving urban problems by means of fuzzy approach, World Academy of Science, Engineering and Technology, vol. 36, pp. 894–901, 2009.
Search in Google Scholar Back to article
[17] D. Toti, AQUEOS: A system for question answering over semantic data, in Proceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014, pp. 716–719, 2014.10.1109/INCoS.2014.13
Search in Google Scholar Back to article
[18] D. Milne and I. H. Witten, An open-source toolkit for mining wikipedia, Artif. Intell., vol. 194, pp. 222–239, Jan. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.artint.2012.06.00710.1016/j.artint.2012.06.007
Open DOI Search in Google Scholar Back to article
[19] N. Capuano, C. De Maio, S. Salerno, and D. Toti, A methodology based on commonsense knowledge and ontologies for the automatic classification of legal cases, in ACM International Conference Proceeding Series, 2014.10.1145/2611040.2611048
Search in Google Scholar Back to article
[20] N. Capuano, A. Longhi, S. Salerno, and D. Toti, Ontology-driven generation of training paths in the legal domain, International Journal of Emerging Technologies in Learning, vol. 10, no. 7, pp. 14–22, 2015.10.3991/ijet.v10i7.4609
Search in Google Scholar Back to article
[21] V. Basile and M. Nissim, Sentiment analysis on Italian tweets, in Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Atlanta, Georgia: Association for Computational Linguistics, pp. 100–107, 2013. [Online]. Available: http://www.aclweb.org/anthology/W13-1614
Search in Google Scholar Back to article
[22] R. R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking, Systems, Man and Cybernetics, IEEE Transactions on, vol. 18, no. 1, pp. 183–190, 1988.10.1109/21.87068
Search in Google Scholar Back to article
[23] T.-A. Shiau and J.-S. Liu, Developing an indicator system for local governments to evaluate transport sustainability strategies, Ecological Indicators, vol. 34, pp. 361 – 371, 2013.10.1016/j.ecolind.2013.06.001
Open DOI Search in Google Scholar Back to article

An Environment for Collective Perception based on Fuzzy and Semantic Approaches

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