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        <title>A Relation Extraction Dataset for Knowledge Extraction from Web Tables</title>
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    <name type="personal">
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    <abstract>Relational web-tables are significant sources of structural information that are widely used for relation extraction and population of facts into knowledge graphs. To transform the web-table data into knowledge, we need to identify the relations that exist between column pairs. Currently, there are only a handful of publicly available datasets with relations annotated against natural web-tables. Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set. In this paper, we present REDTab, the largest natural-table relation extraction dataset. We have annotated ~9K tables and ~22K column pairs using crowd sourced annotators from MTurk, which has 50x larger number of column pairs than the existing human-annotated benchmark. Our test set is specially designed to be challenging as observed in our experiment results using TaBERT. We publicly release REDTab as a benchmark for the evaluation process in relation extraction.</abstract>
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%0 Conference Proceedings
%T A Relation Extraction Dataset for Knowledge Extraction from Web Tables
%A Singh, Siffi
%A Aji, Alham Fikri
%A Singh, Gaurav
%A Christodoulopoulos, Christos
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F singh-etal-2022-relation
%X Relational web-tables are significant sources of structural information that are widely used for relation extraction and population of facts into knowledge graphs. To transform the web-table data into knowledge, we need to identify the relations that exist between column pairs. Currently, there are only a handful of publicly available datasets with relations annotated against natural web-tables. Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set. In this paper, we present REDTab, the largest natural-table relation extraction dataset. We have annotated ~9K tables and ~22K column pairs using crowd sourced annotators from MTurk, which has 50x larger number of column pairs than the existing human-annotated benchmark. Our test set is specially designed to be challenging as observed in our experiment results using TaBERT. We publicly release REDTab as a benchmark for the evaluation process in relation extraction.
%U https://aclanthology.org/2022.coling-1.203
%P 2319-2327

Markdown (Informal)

[A Relation Extraction Dataset for Knowledge Extraction from Web Tables](https://aclanthology.org/2022.coling-1.203) (Singh et al., COLING 2022)

• A Relation Extraction Dataset for Knowledge Extraction from Web Tables (Singh et al., COLING 2022)

ACL

• Siffi Singh, Alham Fikri Aji, Gaurav Singh, and Christos Christodoulopoulos. 2022. A Relation Extraction Dataset for Knowledge Extraction from Web Tables. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2319–2327, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.