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Generating Table Vector Representations

2021-10-28 14:05:21
Aneta Koleva, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
arXiv_CL
           
Abstract

High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG). The focus of this paper is an evaluation of methods for table-to-class annotation, which is a sub-task of Table Interpretation (TI). We provide a formal definition for table classification as a machine learning task. We propose an experimental setup and we evaluate 5 fundamentally different approaches to find the best method for generating vector table representations. Our findings indicate that although transfer learning methods achieve high F1 score on the table classification task, dedicated table encoding models are a promising direction as they appear to capture richer semantics.

Abstract (translated)

URL

https://arxiv.org/abs/2110.15132

PDF

https://arxiv.org/pdf/2110.15132.pdf

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