Ontology-based context modeling for emotion recognition in an intelligent web

We describe an ontological model for representation and integration of electroencephalographic (EEG) data and apply it to detect human emotional states. The model (BIO_EMOTION) is an ontology-based context model for emotion recognition and acts as a basis for: (1) the modeling of users’ contexts, including user profiles, EEG data, the situation and environment factors, and (2) supporting reasoning on the users’ emotional states. Because certain ontological concepts in the EEG domain are ill-defined, we formally represent and store these concepts, their taxonomies and high-level representation (i.e., rules) in the model. To evaluate the effectiveness for inferring emotional states, DEAP dataset is used for model reasoning. Result shows that our model reaches an average recognition ratio of 75.19 % on Valence and 81.74 % on Arousal for eight participants. As mentioned above, the BIO-EMOTION model acts like a bridge between users’ emotional states and low-level bio-signal features. It can be integrated in user modeling techniques, and be used to model web users’ emotional states in human-centric web aiming to provide active, transparent, safe and reliable services to users. This work aims at, in other words, creating an ontology-based context model for emotion recognition using EEG. Particularly, this model completely implements the loop body of the W2T data cycle once: from low-level EEG feature acquisition to emotion recognition. A long-term goal for the study is to complete this model to implement the whole W2T data cycle.