research-article

SAND: Semantic Annotation of Numeric Data in Web Tables

Authors:

Behrad Khorram NazariAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 2342 - 2351

https://doi.org/10.1145/3583780.3615046

Published: 21 October 2023 Publication History

Abstract

A large portion of quantitative information about entities is expressed as Web tables, and these tables often lack proper schema and annotation, which introduces challenges for the purpose of querying and analysis. In this paper, we introduce SAND, a novel approach for annotating numeric columns of Web tables by linking them to properties in a knowledge graph. Our approach relies only on the semantic information readily available in knowledge graphs and not on contextual information that can be missing or labelled data which may be difficult to obtain. We show that our approach can reliably detect both semantic types (e.g., height) and unit labels (e.g., Centimeter) when the semantic type is present in the knowledge graph. Our evaluation on real-world web tables shows that our method outperforms by a large margin, in terms of accuracy, some of the state-of-the-art approaches on semantic labeling and unit detection.

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Cited By

Borrero-Foncubierta ARodriguez-Garcia MMuñoz ADodero J(2024)Protecting privacy in the age of big data: exploring data linking methods for quasi-identifier selectionInternational Journal of Information Security10.1007/s10207-024-00944-724:1Online publication date: 3-Dec-2024
https://doi.org/10.1007/s10207-024-00944-7

Index Terms

SAND: Semantic Annotation of Numeric Data in Web Tables
1. Information systems

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CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

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Published: 21 October 2023

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Cited By

Borrero-Foncubierta ARodriguez-Garcia MMuñoz ADodero J(2024)Protecting privacy in the age of big data: exploring data linking methods for quasi-identifier selectionInternational Journal of Information Security10.1007/s10207-024-00944-724:1Online publication date: 3-Dec-2024
https://doi.org/10.1007/s10207-024-00944-7

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