  Understanding manipulation scenarios allows intelligent robots to plan for
appropriate actions to complete a manipulation task successfully. It is
essential for intelligent robots to semantically interpret manipulation
knowledge by describing entities, relations and attributes in a structural
manner. In this paper, we propose an implementing framework to generate
high-level conceptual dynamic knowledge graphs from video clips. A combination
of a Vision-Language model and an ontology system, in correspondence with
visual perception and contextual semantics, is used to represent robot
manipulation knowledge with Entity-Relation-Entity (E-R-E) and
Entity-Attribute-Value (E-A-V) tuples. The proposed method is flexible and
well-versed. Using the framework, we present a case study where robot performs
manipulation actions in a kitchen environment, bridging visual perception with
contextual semantics using the generated dynamic knowledge graphs.
