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Papers/Learning Semantic Annotations for Tabular Data

Learning Semantic Annotations for Tabular Data

Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

2019-05-30Column Type AnnotationPredictionType predictionTable annotation
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
Data IntegrationT2Dv2Accuracy (%)96.6HNN + P2Vec
Data IntegrationWikipediaGS-CTAAccuracy (%)65.5HNN
Table annotationT2Dv2Accuracy (%)96.6HNN + P2Vec
Table annotationWikipediaGS-CTAAccuracy (%)65.5HNN

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