Facilitating NeuroIS Research Using Natural Language Processing: Towards Automated Recommendations

Nevena Nikolajevic (), Michael T. Knierim () and Christof Weinhardt ()
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Nevena Nikolajevic: Karlsruhe Institute of Technology
Michael T. Knierim: Karlsruhe Institute of Technology
Christof Weinhardt: Karlsruhe Institute of Technology

A chapter in Information Systems and Neuroscience, 2022, pp 147-154 from Springer

Abstract: 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.

Keywords: Recommender systems; Knowledge graphs; Natural language processing; Literature mapping (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-13064-9_15

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DOI: 10.1007/978-3-031-13064-9_15

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