1. Ontology-based high-level context inference for human behavior identification
- Author
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Claudia Villalonga, Sungyoung Lee, Héctor Pomares, Oresti Banos, Ignacio Rojas, Muhammad Asif Razzaq, and Wajahat Ali Khan
- Subjects
METIS-318538 ,Computer science ,Emotions ,Inference ,Context (language use) ,02 engineering and technology ,IR-101559 ,Ontology (information science) ,lcsh:Chemical technology ,Machine learning ,computer.software_genre ,Biochemistry ,Article ,Analytical Chemistry ,context recognition ,context inference ,ontologies ,ontologicalreasoning ,human behavior identification ,activities ,locations ,emotions ,Text mining ,human behavioridentification ,0202 electrical engineering, electronic engineering, information engineering ,Ontologies ,lcsh:TP1-1185 ,ontological reasoning ,Electrical and Electronic Engineering ,Set (psychology) ,Instrumentation ,EWI-27261 ,Event (computing) ,business.industry ,Scale (chemistry) ,020206 networking & telecommunications ,Atomic and Molecular Physics, and Optics ,Identification (information) ,Ontology ,020201 artificial intelligence & image processing ,Data mining ,Artificial intelligence ,business ,computer - Abstract
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.
- Published
- 2016
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