  We demonstrate a library for the integration of domain knowledge in deep
learning architectures. Using this library, the structure of the data is
expressed symbolically via graph declarations and the logical constraints over
outputs or latent variables can be seamlessly added to the deep models. The
domain knowledge can be defined explicitly, which improves the models'
explainability in addition to the performance and generalizability in the
low-data regime. Several approaches for such an integration of symbolic and
sub-symbolic models have been introduced; however, there is no library to
facilitate the programming for such an integration in a generic way while
various underlying algorithms can be used. Our library aims to simplify
programming for such an integration in both training and inference phases while
separating the knowledge representation from learning algorithms. We showcase
various NLP benchmark tasks and beyond. The framework is publicly available at
Github(https://github.com/HLR/DomiKnowS).
