Ontology-Based High-Level Context Inference for Human Behavior Identification Villalonga Palliser, Claudia Razzaq, Muhammad Asif Khan, Wajahat Ali Pomares Cintas, Héctor Emilio Rojas Ruiz, Ignacio Lee, Sungyoung Baños Legrán, Oresti Context recognition Context inference Ontologies Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users. 2024-10-01T10:28:30Z 2024-10-01T10:28:30Z 2016-09-29 journal article Villalonga, C.; Razzaq, M.A.; Khan, W.A.; Pomares, H.; Rojas, I.; Lee, S.; Banos, O. Ontology-Based High-Level Context Inference for Human Behavior Identification. Sensors 2016, 16, 1617. https://doi.org/10.3390/s16101617 https://hdl.handle.net/10481/95345 10.3390/s16101617 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI