Towards Learned Metadata Extraction for Data Lakes

Abstract

An important task for enabling the efficient exploration of available data in a data lake is to annotate semantic type information to the available data sources. In order to reduce the manual overhead of annotation, learned approaches for automatic metadata extraction on structured data sources have been proposed recently. While initial results of these learned approaches seem promising, it is still not clear how well these approaches can generalize to new unseen data in real-world data lakes. In this paper, we aim to tackle this question and as a first contribution show the result of a study when applying Sato -a recent approach based on deep learning -to a real-world data set. In our study we show that Sato is only able to extract semantic data types for about 10% of the columns of the real-world data set. These results show the general limitation of deep learning approaches which often provide near-perfect performance on available training and testing data but fail in real settings since training data and real data often strongly vary. Hence, as a second contribution we propose a new direction of using weak supervision and present results of an initial prototype we built to generate labeled training data with low manual efforts to improve the performance of learned semantic type extraction approaches on new unseen data sets.