Learning Semantic Annotations for Tabular Data
This work addresses the challenge of understanding semantics in tabular data for applications like web tables, representing an incremental improvement over traditional methods.
The study tackled the problem of predicting column types for tables lacking metadata by proposing a deep prediction model that leverages contextual semantics, achieving good performance on individual table sets and in transfer scenarios.
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.