Smart City Monitoring Using Ontology-based Machine Learning

From ISLAB/CAISR
Title Smart City Monitoring Using Ontology-based Machine Learning
Summary The aim of this project is to create ontology-based supervised and unsupervised machine learning methods for self monitoring to improve reliability of complex environments in smart cities
Keywords Data Mining, Knowledge Discovery
TimeFrame
References [[References::[1] Kassahun, Y., Perrone, R., De Momi, E., Berghöfer, E., Tassi, L., Canevini, M.P., Spreafico, R., Ferrigno, G. and Kirchner, F., 2014. Automatic classification of epilepsy types using ontology-based and genetics-based machine learning. Artificial intelligence in medicine, 61(2), pp.79-88.

[2] Cheong, Y.G., Kim, Y.J., Yoo, S.Y., Lee, H., Lee, S., Chae, S.C. and Choi, H.J., 2011, January. An ontology-based reasoning approach towards energy-aware smart homes. In 2011 IEEE Consumer Communications and Networking Conference (CCNC) (pp. 850-854). IEEE.

[3] Middleton, Stuart E., David C. De Roure, and Nigel R. Shadbolt. "Capturing knowledge of user preferences: ontologies in recommender systems." Proceedings of the 1st international conference on Knowledge capture. ACM, 2001.

[4] Rudin, C., Waltz, D., Anderson, R.N., Boulanger, A., Salleb-Aouissi, A., Chow, M., Dutta, H., Gross, P.N., Huang, B., Ierome, S. and Isaac, D.F., 2012. Machine learning for the New York City power grid. IEEE transactions on pattern analysis and machine intelligence, 34(2), pp.328-345.]]

Prerequisites Good knowledge of machine learning and programming skills for implementing machine learning algorithms
Author
Supervisor Sławomir Nowaczyk, Ece Calikus
Level Master
Status Open

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Ontological modelling provides an insight of a specific knowledge domain and it is made of classes, relationships and instances. Ontologies made of hierarchies and properties between classes can be useful for data aggregation and clustering. Such ontologies provide domain knowledge and support the interpretation of relations identified in dataset through data mining processes, based on statistical techniques. Therefore, ontology-based machine learning approaches can directly incorporate human knowledge.

In smart city field, ontologies can be used for sharing city knowledge in a reliable format so that it is understandable and can be processed by both humans and machines. The aim of this project is to create ontology-based supervised and unsupervised machine learning methods for self monitoring to improve reliability of complex environments in smart cities. Furthermore, we will use data obtained from various industrial partners such as Volvo AB, HMS, HEM, Alfa Laval etc. in order to combine different domain knowledges (smart vehicles, district heating, industrial networks and etc.) hierarchically for smart cities.