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Data Extraction Using NLP Techniques and Its Transformation to Linked Data

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Human-Inspired Computing and Its Applications (MICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8856))

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Abstract

We present a system that extracts a knowledge base from raw unstructured texts that is designed as a set of entities and their relations and represented in an ontological framework. The extraction pipeline processes input texts by linguistically-aware tools and extracts entities and relations from their syntactic representation. Consequently, the extracted data is represented according to the Linked Data principles. The system is designed both domain and language independent and provides users with data for more intelligent search than full-text search. We present our first case study on processing Czech legal texts.

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Author information

Authors and Affiliations

  1. Institute of Formal and Applied Linguistics, Charles University in Prague, Malostranské nám. 25, 118 00, Praha 1, Czech Republic

    Vincent Kríž & Barbora Hladká

  2. Department of Software Engineering Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 25, 118 00, Praha 1, Czech Republic

    Martin Nečaský & Tomáš Knap

Authors
  1. Vincent Kríž
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  2. Barbora Hladká
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  3. Martin Nečaský
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  4. Tomáš Knap
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  2. Área Académica de Computación y Electrónica, Carretera Pachuca-Tulancingo, Universidad Autónoma del Estado de Hidalgo, Km. 4.5, Col. Carboneras, Mineral de la Reforma, 42180, Hidalgo, Mexico

    Félix Castro Espinoza

  3. Facultad de ciencias, Universidad Autónoma Nacional de México, Ciudad Universitaria, México DF, Mexico

    Sofía N. Galicia-Haro

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Kríž, V., Hladká, B., Nečaský, M., Knap, T. (2014). Data Extraction Using NLP Techniques and Its Transformation to Linked Data. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-13647-9_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13646-2

  • Online ISBN: 978-3-319-13647-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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