  Analyzing ethnic or religious bias is important for improving fairness,
accountability, and transparency of natural language processing models.
However, many techniques rely on human-compiled lists of bias terms, which are
expensive to create and are limited in coverage. In this study, we present a
fully data-driven pipeline for generating a knowledge graph (KG) of cultural
knowledge and stereotypes. Our resulting KG covers 5 religious groups and 5
nationalities and can easily be extended to include more entities. Our human
evaluation shows that the majority (59.2%) of non-singleton entries are
coherent and complete stereotypes. We further show that performing intermediate
masked language model training on the verbalized KG leads to a higher level of
cultural awareness in the model and has the potential to increase
classification performance on knowledge-crucial samples on a related task,
i.e., hate speech detection.
