1. bookVolume 8 (2018): Issue 3 (July 2018)
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

An Environment for Collective Perception based on Fuzzy and Semantic Approaches

Published Online: 09 Feb 2018
Volume & Issue: Volume 8 (2018) - Issue 3 (July 2018)
Page range: 191 - 210
Received: 05 Sep 2017
Accepted: 30 Aug 2017
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
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.

Keywords

[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

[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

[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-150683Search in Google Scholar

[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/s130100393Search in Google Scholar

[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/ISC2.2016.7580873Search in Google Scholar

[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

[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

[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.2016.30Search in Google Scholar

[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

[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.2016.7737875Search in Google Scholar

[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.2012.2227263Search in Google Scholar

[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)80040-2Search in Google Scholar

[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.2015.01.061Search in Google Scholar

[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.2015.06.006Search in Google Scholar

[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.2003.10.027Search in Google Scholar

[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

[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.2014.13Search in Google Scholar

[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.2012.06.007Open DOISearch in Google Scholar

[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, 2014.10.1145/2611040.2611048Search in Google Scholar

[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.v10i7.4609Search in Google Scholar

[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/W13-1614Search in Google Scholar

[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, 1988.10.1109/21.87068Search in Google Scholar

[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.2013.06.001Open DOISearch in Google Scholar

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