City Research Online

Abstract

Tabular data, often found in CSV files, is essential for data analytics workflows. Understanding this data in a semantic context, known as Semantic Table Interpretation (STI), is critical but challenging due to issues like label ambiguity. Consequently, STI has garnered significant attention in recent years. To evaluate STI systems effectively, robust benchmarks are needed. Most existing large-scale benchmarks originate from general domain sources and emphasize ambiguity, whereas domain-specific benchmarks tend to be smaller. This paper presents KG2Tables, a framework designed to create large-scale domain-specific benchmarks from a Knowledge Graph (KG). KG2Tables utilizes the internal hierarchy of relevant KG concepts and their properties. As a proof of concept, we have developed extensive datasets in the food, biodiversity, and biomedical domains. One of these datasets was used in the ISWC 2023 SemTab challenge, and the rest have been integrated into SemTab 2024.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher Keywords:	Semantic Table Interpretation (STI), Knowledge Graph (KG), Tabular Data, Benchmark, SemTab
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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