In this paper, we introduce STEER to adapt learned semantic type extraction approaches to a new, unseen data lake. STEER provides a data programming framework for semantic labeling which is used to generate new labeled training data with minimal overhead. At its core, STEER comes with a novel training data generation procedure called Steered-Labeling that can generate high quality training data not only for non-numeric but also for numerical columns. With this generated training data STEER is able to fine-tune existing learned semantic type extraction models. We evaluate our approach on four different data lakes and show that we can significantly improve the performance of two different types of learned models across all data lakes.

Ontologies are increasingly used to represent the intended real-world semantics of data and services in information systems. Unfortunately, different data sources often do not relate to the same ontologies when describing their semantics. Consequently, it is desirable to have information about the similarity between ontology concepts for ontology alignment and integration. In this demo, we present the SOQA-SimPack Toolkit (SST), an ontology language independent Java API that enables generic similarity detection and visualization in ontologies. We demonstrate SST's usefulness with the SOQA-SimPack Toolkit Browser that allows users to graphically perform similarity calculations in ontologies.