  It is commonly acknowledged that the availability of the huge amount of
(training) data is one of the most important factors for many recent advances
in Artificial Intelligence (AI). However, datasets are often designed for
specific tasks in narrow AI sub areas and there is no unified way to manage and
access them. This not only creates unnecessary overheads when training or
deploying Machine Learning models but also limits the understanding of the
data, which is very important for data-centric AI. In this paper, we present
our vision about a unified framework for different datasets so that they can be
integrated and queried easily, e.g., using standard query languages. We
demonstrate this in our ongoing work to create a framework for datasets in
Computer Vision and show its advantages in different scenarios. Our
demonstration is available at https://vision.semkg.org.
