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Efficient High-Level Semantic Enrichment of Undocumented Enterprise Data

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The Semantic Web: ESWC 2019 Satellite Events (ESWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11762))

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Abstract

In absence of a data management strategy, undocumented enterprise data piles up and becomes increasingly difficult for companies to use to its full potential. As a solution, we propose the enrichment of such data with meaning, or more precisely, the interlinking of data content with high-level semantic concepts. In contrast to low-level data lifting and mid-level information extraction, we would like to reach a high level of knowledge conceptualization. Currently, this can only be achieved if human experts are integrated into the enrichment process. Since human expertise is costly and limited, our methodology is designed to be as efficient as possible. That includes quantifying enrichment levels as well as assessing efficiency of gathering and exploiting user feedback. This paper proposes research on how semantic enrichment of undocumented enterprise data with humans in the loop can be conducted. We already got promising preliminary results from several projects in which we enriched various enterprise data.

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Notes

  1. 1.

    http://www.w3.org/TR/r2rml.

  2. 2.

    http://www.w3.org/TR/grddl.

  3. 3.

    http://www.pro-opt.org/.

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Acknowledgements

Parts of this work have been funded by the German Federal Ministry of Economic Affairs and Energy in the project PRO-OPT (01MD15004D) and by the German Federal Ministry of Food and Agriculture in the project SDSD (2815708615). I thank my doctoral supervisor Prof. Dr. Andreas Dengel and my colleagues Christian Jilek, Dr. Heiko Maus, Dr. Sven Schwarz, Dr. Jörn Hees and Dr. Ansgar Bernardi for their helpful discussions, comments and feedback.

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Authors and Affiliations

  1. Smart Data & Knowledge Services Department, DFKI GmbH, Kaiserslautern, Germany

    Markus Schröder

  2. Computer Science Department, TU Kaiserslautern, Kaiserslautern, Germany

    Markus Schröder

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  1. Markus Schröder
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Correspondence to Markus Schröder .

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Editors and Affiliations

  1. Kansas State University, Manhattan, KS, USA

    Pascal Hitzler

  2. Vienna University of Economics and Business, Vienna, Austria

    Sabrina Kirrane

  3. Linköping University, Linköping, Sweden

    Olaf Hartig

  4. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Victor de Boer

  5. Leibniz Information Centre for Science and Technology University Library (TIB), Hannover, Germany

    Maria-Esther Vidal

  6. University of Bonn, Bonn, Germany

    Maria Maleshkova

  7. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Stefan Schlobach

  8. Jönköping University, Jönköping, Sweden

    Karl Hammar

  9. F. Hoffmann-La Roche AG, Basel, Switzerland

    Nelia Lasierra

  10. Robert Bosch GmbH, Stuttgart, Germany

    Steffen Stadtmüller

  11. Aalborg University, Aalborg, Denmark

    Katja Hose

  12. IMEC, Ghent University, Ghent, Belgium

    Ruben Verborgh

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Schröder, M. (2019). Efficient High-Level Semantic Enrichment of Undocumented Enterprise Data. In: Hitzler, P., et al. The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science(), vol 11762. Springer, Cham. https://doi.org/10.1007/978-3-030-32327-1_41

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  • DOI: https://doi.org/10.1007/978-3-030-32327-1_41

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