Steered Training Data Generation for Learned Semantic Type Detection
Authors: 
Sven Langenecker, Christoph Sturm, Christian Schalles Schalles, Carsten BinnigAuthors Info & Claims
Sven Langenecker, Christoph Sturm, Christian Schalles Schalles, Carsten BinnigAuthors Info & ClaimsArticle No.: 201, Pages 1 - 25
Abstract
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.
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Presentation video of the paper "Steered Training Data Generation For Learned Semantic Type Detection" by Sven Langenecker, Christoph Sturm, Christian Schalles und Carsten Binnig. The presentation is about a new labeling framework for the task of semantic type detection that comes with a novel Steered-Labeling procedure to generate high quality training data for non-numeric as well as for numeric data table-columns.
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Published: 20 June 2023
Published in PACMMOD Volume 1, Issue 2
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