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Learning Semantic Annotations for Tabular Data

Chen, J., Horrocks, I., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599 and Sutton, C. (2019). Learning Semantic Annotations for Tabular Data. In: Kraus, S. (Ed.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. (pp. 2088-2094). International Joint Conferences on Artifical Intelligence (IJCAI). ISBN 978-0-9992411-4-1

Abstract

The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantics, including table locality features learned by a Hybrid Neural Network (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.It exhibits good performance not only on individual table sets, but also when transferring from one table set to another.

Publication Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Copyright © 2019 International Joint Conferences on Artificial Intelligence
Publisher Keywords: Machine Learning: Knowledge-based Learning Multidisciplinary Topics and Applications: Intelligent Database Systems Natural Language Processing: Knowledge Extraction Knowledge Representation and Reasoning: Description Logics and Ontologies Natural Language Processing: Embeddings Machine Learning Applications: Applications of Supervised Learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: https://openaccess.city.ac.uk/id/eprint/22929
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