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Facilitating NeuroIS Research Using Natural Language Processing: Towards Automated Recommendations

In: Information Systems and Neuroscience

Author

Listed:
  • Nevena Nikolajevic

    (Karlsruhe Institute of Technology)

  • Michael T. Knierim

    (Karlsruhe Institute of Technology)

  • Christof Weinhardt

    (Karlsruhe Institute of Technology)

Abstract

Facing rapidly growing numbers of scientific publications, attempts to structure and interconnect scientific articles are becoming increasingly relevant. Various approaches to scientific article mapping are discussed in academic literature. However, these are mainly developed upon citation-based similarity measures. We propose an extension to these concepts of literature mapping by introducing a knowledge graph-based approach. Our contribution is twofold. We introduce a holistic concept to systematically capture literature in the field of NeuroIS research based on a relational mapping of main research concepts in the field. Additionally, we implement a content-based recommender system for literature in the NeuroIS domain that serves us as a proof-of-concept and a performance baseline for further developments. By providing a relational overview of the research domain and a dedicated recommender system, we hope to facilitate the entry to the field for new researchers and enhance further research.

Suggested Citation

  • Nevena Nikolajevic & Michael T. Knierim & Christof Weinhardt, 2022. "Facilitating NeuroIS Research Using Natural Language Processing: Towards Automated Recommendations," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 147-154, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-13064-9_15
    DOI: 10.1007/978-3-031-13064-9_15
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