Service and industrial robots are expected to be more autonomous and work effectively around/ alongside humans. This implies that robots should have special capabilities, such as interpreting and understanding human intentions in different domains. The major challenge is to find appropriate mechanisms to explain the observed raw sensor signals such as poses, velocities, distances, forces, etc., in a way that robots are able to make informative and high-level descriptive models out of that. These models will for instance permit the understanding of, what is the meaning of the observations/demonstrations, infer how they could generate/produce a similar behavior in other conditions/domains?, and more importantly, allow robots to communicate with the user/operator about why they infer that behavior. One promising way to achieve that is using high-level semantic representations. Several methods have been proposed, for example, linguistic approaches, syntactic approaches, graphical models, etc. Even though these methods have achieved robust performance, one of the missing components is the lack of common measurements to compare the proposed techniques in established bench-marking data sets, due to the fact that they are not publicly available.