City Research Online

Abstract

2025 continues the mission of benchmarking semantic table interpretation systems in an era shaped by large language models (LLMs) and retrieval-augmented strategies. This edition introduces two complementary tracks. The first is the large-scale MammoTab dataset for cell entity annotation (CEA) aligned to Wikidata. The second is the new Secu-Table Track designed to evaluate robustness under noisy, adversarial, and security-domain table conditions. This paper presents an overview of the challenge setup, datasets, evaluation methodology, and the key outcomes of this edition.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© The Authors. Published by CEUR. This is an open-access article distributed under the terms of Creative Commons: Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	Tabular data, Knowledge Graphs, Matching, SemTab Challenge, Semantic Table Interpretation, Large Language Models, LLMs
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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