  How can we leverage existing column relationships within silos, to predict
similar ones across silos? Can we do this efficiently and effectively? Existing
matching approaches do not exploit prior knowledge, relying on prohibitively
expensive similarity computations. In this paper we present the first technique
for matching columns across data silos, called SiMa, which leverages Graph
Neural Networks (GNNs) to learn from existing column relationships within data
silos, and dataset-specific profiles. The main novelty of SiMa is its ability
to be trained incrementally on column relationships within each silo
individually, without requiring the consolidation of all datasets in a single
place. Our experiments show that SiMa is more effective than the - otherwise
inapplicable to the setting of silos - state-of-the-art matching methods, while
requiring orders of magnitude less computational resources. Moreover, we
demonstrate that SiMa considerably outperforms other state-of-the-art column
representation learning methods.
