A sensor networked environment is capable of observing real-world phenomena as sensor readings, however, there are difficulties in understanding real-world ‘events’ such as activities of daily life occurring in the environment. We propose an incremental model for constructing real-world knowledge to allow us to understand real-world events. The central plank of the proposed model is the labeling practice. In the model, both the ontology of real-world events and the implementation of a sensor system are simultaneously improved based on a manually labeled event corpus. A labeling tool is developed in accordance with the model and event vocabularies are evaluated in a practical labeling experiment.
['Koji Kamei', 'Yutaka Yanagisawa', 'Takuya Maekawa', 'Yasue Kishino', 'Yasushi Sakurai', 'Takeshi Okadome']
Labeling-based knowledge construction for real-world event understanding