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Making Structured Data Searchable via Natural Language Generation

with an Application to ESG Data

  • Conference paper
Flexible Query Answering Systems (FQAS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8132))

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Abstract

Relational Databases are used to store structured data, which is typically accessed using report builders based on SQL queries. To search, forms need to be understood and filled out, which demands a high cognitive load. Due to the success of Web search engines, users have become acquainted with the easier mechanism of natural language search for accessing unstructured data. However, such keyword-based search methods are not easily applicable to structured data, especially where structured records contain non-textual content such as numbers.

We present a method to make structured data, including numeric data, searchable with a Web search engine-like keyword search access mechanism. Our method is based on the creation of surrogate text documents using Natural Language Generation (NLG) methods that can then be retrieved by off-the-shelf search methods.

We demonstrate that this method is effective by applying it to two real-life sized databases, a proprietary database comprising corporate Environmental, Social and Governance (ESG) data and a public-domain environmental pollution database, respectively, in a federated scenario. Our evaluation includes speed and index size investigations, and indicates effectiveness (P@1 = 84%, P@5 = 92%) and practicality of the method.

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Author information

Authors and Affiliations

  1. Catalyst Lab, Thomson Reuters Global Resources, Neuhofstrasse 1, CH-6340, Baar, Switzerland

    Jochen L. Leidner & Darya Kamkova

Authors
  1. Jochen L. Leidner
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  2. Darya Kamkova
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Editor information

Editors and Affiliations

  1. Department of Electronic Systems, Aalborg University, 6700, Esbjerg, Denmark

    Henrik Legind Larsen

  2. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Maria J. Martin-Bautista

  3. Department of Computer Science and Arificial IntelIigence, University of Granada, 18071, Granada, Spain

    María Amparo Vila

  4. CBIT, Roskilde University, Universitetsvej 1, 4000, Roskilde, Denmark

    Troels Andreasen

  5. CBIT, Roskilde University, 4000, Roskilde, Denmark

    Henning Christiansen

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Leidner, J.L., Kamkova, D. (2013). Making Structured Data Searchable via Natural Language Generation. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_43

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  • DOI: https://doi.org/10.1007/978-3-642-40769-7_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40768-0

  • Online ISBN: 978-3-642-40769-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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