## CEUR-WS.org/Vol-2044/paper1
TableQA: Question Answering on Tabular Data

#### Svitlana Vakulenko, Vadim Savenkov: TableQA: Question Answering on Tabular Data.
In: J. Fernández, S. Hellmann (eds.): Proceedings of the Posters and Demos Track of the 13th International Conference on Semantic Systems - SEMANTiCS2017,
Amsterdam, The Netherlands, September 11-14, 2017,
online: http://ceur-ws.org/Vol-2044/paper1

Abstract:
Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools and without having to fully understand the dataset structure. The goal of our demonstration is to showcase answering natural language questions from tabular data, and to discuss related system configuration and model training aspects. Our prototype is publicly available and open-sourced.
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- Semantic annotations in NLP Interchange Format (RDF/Turtle)
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