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.