Abstract

Understanding the semantic structure of tabular data is essential for data integration and discovery. Specifically, the goal is to annotate columns in a tabular source with types and relationships between them using classes and predicates of a target ontology. Previous work either requires trained labeled data or exploits the overlapping data between the table data and a knowledge graph to predict types and relationships. However, these approaches cannot be used in a new domain with limited labeled data. To address this issue, we propose a novel domain-independent approach to estimate a score reflecting the semantic relatedness between a table column and an ontology class or property using the table metadata and data. Our empirical evaluation demonstrates that our approach significantly outperforms strong baselines based on large language models.

Date

October 29, 2025

Authors

Binh Vu, Craig A Knoblock, Fandel Lin

Book

International Semantic Web Conference

Pages

235-252

Publisher

Springer Nature Switzerland