Advances in language models (LM), specifically deep language understanding capabilities, offer new opportunities to tackle classic data-management problems such as data integration, entity matching, and table discovery. The domain of HR offers us new problems that demand explainability. This field in particular allows us to broaden classic problems to formulate new ones such as generalized entity matching for identifying binary relations between entities of different types with heterogeneous data.
Our work in the AI-for-data-management area has recently focused on exploiting language models and state of the art machine learning approaches. We utilize large language models in novel settings for finding table representations to discover datasets in data lakes, data augmentation techniques for data management tasks, and different declarative explanation approaches for data integration tasks.