Learning semantic annotations for tabular data
arXiv preprint arXiv:1906.00781, 2019•arxiv.org
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 …
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 …
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.
arxiv.org