Back to Search
Start Over
Learning Semantic Annotations for Tabular Data
- Source :
- IJCAI 2019
- Publication Year :
- 2019
-
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.<br />Comment: 7 pages
Details
- Database :
- arXiv
- Journal :
- IJCAI 2019
- Publication Type :
- Report
- Accession number :
- edsarx.1906.00781
- Document Type :
- Working Paper