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 NeuralNetwork (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.
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
