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Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles

  • Said Fathalla
  • Sahar Vahdati
  • Sören Auer
  • Christoph Lange
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10450)

Abstract

Despite significant advances in technology, the way how research is done and especially communicated has not changed much. We have the vision that ultimately researchers will work on a common knowledge base comprising comprehensive descriptions of their research, thus making research contributions transparent and comparable. The current approach for structuring, systematizing and comparing research results is via survey or review articles. In this article, we describe how surveys for research fields can be represented in a semantic way, resulting in a knowledge graph that describes the individual research problems, approaches, implementations and evaluations in a structured and comparable way. We present a comprehensive ontology for capturing the content of survey articles. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the resulting knowledge graph by using it to answer queries about the different research contributions covered by the survey and evaluate how well the query answers serve readers’ information needs, in comparison to having them extract the same information from reading a survey paper.

Keywords

Semantic metadata enrichment Quality assessment Recommendation services Scholarly communication Semantic publishing 

Notes

Acknowledgments

This work has been supported by the H2020 project no. 645833 (OpenBudgets.eu). The authors would like to thank Prof. Maria-Esther Vidal and Afshin Sadeghi for their support. We also appreciate the help of all participants of the evaluation. This work was conducted using the Protégé resource, which is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Said Fathalla
    • 1
    • 3
  • Sahar Vahdati
    • 1
  • Sören Auer
    • 4
    • 5
  • Christoph Lange
    • 1
    • 2
  1. 1.Smart Data Analytics (SDA)University of BonnBonnGermany
  2. 2.Fraunhofer IAISSankt AugustinGermany
  3. 3.Faculty of ScienceAlexandria UniversityAlexandriaEgypt
  4. 4.Computer ScienceLeibniz University of HannoverHanoverGermany
  5. 5.TIB Leibniz Information Center for Science and TechnologyHannoverGermany

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