Learning Semantic Annotations for Tabular Data

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

IJCAI 2019 pp. 2088-2094

doi:10.24963/IJCAI.2019/289 /ijcai/2019/chen2019ijcai-learning-a/

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.

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Cite

Text

Chen et al. "Learning Semantic Annotations for Tabular Data." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/289

Markdown

[Chen et al. "Learning Semantic Annotations for Tabular Data." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/chen2019ijcai-learning-a/) doi:10.24963/IJCAI.2019/289

BibTeX

@inproceedings{chen2019ijcai-learning-a,
  title     = {{Learning Semantic Annotations for Tabular Data}},
  author    = {Chen, Jiaoyan and Jiménez-Ruiz, Ernesto and Horrocks, Ian and Sutton, Charles},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {2088-2094},
  doi       = {10.24963/IJCAI.2019/289},
  url       = {https://mlanthology.org/ijcai/2019/chen2019ijcai-learning-a/}
}