Abstract

There is an enormous number of tables available on the Web. However, it is difficult to automatically use the tables in data analytic pipelines because of the lack of semantic understanding of their structure and meaning. To address this problem, our approach, GRAMS+, automatically creates semantic descriptions of tables using distant supervision. SemTab is an annual challenge that provides a diverse set of benchmarks for systems that match tabular data with knowledge graphs. In this paper, we present the results of GRAMS+ at SemTab 2024 in the Accuracy Track. The results show that GRAMS+ is scalable and achieves competitive performance in the tasks in which we participated.

Date

July 18, 2025

Authors

Binh Vu, C Knoblock, Fandel Lin

Volume

24

Publisher

SemTab