Following work on joint object-action representations, the functional object-oriented network (FOON) was introduced as a knowledge graph representation for robots. Taking the form of a bipartite graph, a FOON contains symbolic (high-level) concepts pertinent to a robot's understanding of its environment and tasks in a way that mirrors human understanding of actions. However, little work has been done to demonstrate how task plans acquired from FOON can be used for task execution by a robot, as the concepts typically found in a FOON are too abstract for immediate execution. To address this, we incorporate a hierarchical task planning approach to translate a FOON graph into a PDDL-based representation of domain knowledge for manipulation planning. As a result of this process, a task plan can be acquired that a robot can execute from start to end, leveraging the use of action contexts and motion primitives in the form of dynamic movement primitives (DMP). Learned action contexts can then be extended to never-before-seen scenarios.