Activity models play a critical role for activity recognition and assistance in ambient assisted living. Existing approaches to activity modeling suffer from a number of problems, e.g., cold-start, model reusability, and incompleteness. In an effort to address these problems, we introduce an ontology-based hybrid approach to activity modeling that combines domain knowledge based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are deployed, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this paper focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. The approach has been implemented in a feature-rich assistive living system. Analysis of the experiments conducted has been undertaken in an effort to test and evaluate the activity learning algorithms and associated mechanisms.