Hemiparesis is the most common impairment after stroke, the leading cause of adult disability in the United States. The initial severity of hemiparesis had been the strongest predictor of neuromotor functional recovery level. However, the intervention response of stroke survivors does not always correlate with their initial level of impairment. This implies the existence of other factors that may significantly affect stroke survivors' recovery process. In order to design targeting intervention therapy strategies, it is critical to consider these factors in a principled, comprehensive way so that physical rehabilitation (PR) researchers may predict which stroke survivors will respond best to therapy and subsequently, determine if a particular type of therapy is a more optimal match. Currently, such prediction is primarily a manual process and remains a challenging task to PR researchers and clinicians. We propose a computing framework based upon a domain-specific ontology. This framework aims to facilitate knowledge acquisition from existing sources via semantics-enhanced data mining (SEDM) techniques. As a result, it will assist PR researchers and clinicians in better predicting stroke survivors' neuromotor functional recovery level, and will help physical therapists customize most etTective intervention therapy plans for individual stroke survivors.