Learning from few examples is considered a very challenging task where transfer learning proved to be beneficial. Such a learning framework exploits previous experiences and knowledge to compensate for the lack of training data in a novel domain. Knowledge representation plays a vital role in the type and performance of transfer learning approaches, as well as its robustness against negative transfer effect. This aspect is usually not considered in most of the proposed transfer learning methodologies, where the focus is either on the transfer type or on the representation. In this work, we study the use of various high-level semantics in transfer metric learning. We propose a generic transfer metric learning framework, and analyze the effect of different semantic similarity spaces on transfer type and efficiency against negative transfer. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the attribute semantic space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach.