Short Paper (5 pages)
Official ACM published version (Open Access): https://doi.org/10.1145/3459637.3482140
Local copy: PDF
Table search aims to retrieve a list of tables given a user's query. Previous methods only consider the textual information of tables and the structural information is rarely used. In this paper, we propose to model the complex relations in the table corpus as one or more graphs and then utilize graph neural networks to learn representations of queries and tables. We show that the text-based table retrieval methods can be further improved by graph-based predictions which fuse multiple field-level information.
In Proceedings of 30th International Conference on Information and Knowledge Management (CIKM), pages 2945-2949, November. DOI: 10.1145/3459637.3482140.
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