Ontology proposal to categorize economic activities in cities of Mexico
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Abstract
In this work, a data-based methodology is proposed to obtain an ontology that describes the predominant economic activities of the municipalities of Mexico. The learned ontology is based on Word embedding techniques and unsupervised learning with data from the National Statistical Directory of Economic Units (DENUE). The proposed model differs from some other ontology proposals that are based on subjective estimates of experts in the smart city domain. In contrast, this proposal determines economic activities that already exist in the municipalities of Mexico and proposes an ontology that describes the municipalities in terms of their development in concepts or dimensions. The main concepts found in this proposal are industry, economy, health, food, communications, culture, environment, mobility, among others. Thus, the ontology measures levels of development (similar or different) in each of these concepts for the municipalities of Mexico. In municipalities with similar levels of development, joint development opportunities can be discovered: what economic units a municipality is missing that another already has. This methodology can be extrapolated to other municipalities or cities in other countries if similar information on economic units is available.
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Alti, A., Lakehal, A., Laborie, S., & Roose, P. (2016). Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments. Future Internet, 8(4), 48. https://doi.org/10.3390/fi8040048
Ageed, Z. S., Zeebaree, S. R. M., Sadeeq, M. M., Kak, S. F., Rashid, Z. N., Salih, A. A., & Abdullah, W. M. (2021). A Survey of Data Mining Implementation in Smart City Applications. Qubahan Academic Journal, 1(2), 91-99. https://doi.org/10.48161/qaj.v1n2a52
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding Smart Cities: An Integrative Framework. 2012 45th Hawaii International Conference on System Sciences, 2289-2297. https://doi.org/10.1109/HICSS.2012.615
De Nicola, A., & Villani, M. L. (2021). Smart City Ontologies and Their Applications: A Systematic Literature Review. Sustainability, 13(10). https://doi.org/10.3390/su13105578
Djenouri, Y., Belhadi, H., Akli-Astouati, K., Cano, A., & Chun-Wei Lin, J. (2022). An ontology matching approach for semantic modeling: A case study in smart cities. Computational Intelligence, 38(3), 876-902. https://doi.org/10.1111/coin.12474
Instituto Nacional de Estadística y Geografía (INEGI). (2023). Directorio Estadístico Nacional de Unidades Económicas: DENUE Interactivo 11/2023: documento metodológico. México: INEGI.
Jalolov, T. S. (2023, November 27). Teaching the Basics of Python Programming. International Multidisciplinary Journal for Research & Development, 10(11). https://www.ijmrd.in/index.php/imjrd/article/view/443
Karani, D. (2018, September 01). Introduction to word embedding and word2vec. Towards Data Science, 1. https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec652d0c2060fa
Malkawi, O., Obaid, N., & Almobaideen, W. (2022). Toward an Ontological Cyberattack Framework to Secure Smart Cities with Machine Learning Support. International Journal of Advanced Computer Science and Applications, 13(11). https://doi.org/10.14569/IJACSA.2022.0131145
Na, S., Xumin, L., & Yong, G. (2010). Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm. 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, 63-67. https://doi.org/10.1109/IITSI.2010.74
Rahayu, N. W., Ferdiana, R., & Kusumawardani, S. S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047. https://doi.org/10.1016/j.caeai.2022.100047