Efficient High-Level Semantic Enrichment of Undocumented Enterprise Data

  • Markus SchröderEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11762)


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.


Semantic enrichment Knowledge graph building Enterprise data Human in the loop 



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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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Smart Data & Knowledge Services DepartmentDFKI GmbHKaiserslauternGermany
  2. 2.Computer Science DepartmentTU KaiserslauternKaiserslauternGermany

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