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Generate FAIR Literature Surveys with Scholarly Knowledge Graphs

Published: 01 August 2020 Publication History
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  • Abstract

    Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG.

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    MP4 File (3383583.3398520.mp4)
    In this presentation, a methodology and implementation are presented for generating FAIR literature surveys using a scholarly knowledge graph. The knowledge graph in which we implement our methodology is called Open Research Knowledge Graph (ORKG). We start with explaining why there is a need for structured scholarly knowledge and what the current challenges are in scholarly communication. Afterwards, the five-step methodology is discussed in which each step is explained in detail. In the evaluation part is explained how the proposed method can be used as alternative to the traditional method of publishing literature surveys. Also, the compliance to the FAIR guidelines is evaluated. Finally, the conclusions and limitations of this research are discussed.

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    cover image ACM Conferences
    JCDL '20: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
    August 2020
    611 pages
    ISBN:9781450375856
    DOI:10.1145/3383583
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    Published: 01 August 2020

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    Author Tags

    1. FAIR data principles
    2. comparison user interface
    3. digital libraries
    4. scholarly communication
    5. scholarly information systems
    6. scholarly knowledge comparison

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    • (2024)RCE (rationale–cogency–extent) criterion unravels features affecting citation impact of top-ranked systematic literature reviews: leaving the impression…is all you needScientometrics10.1007/s11192-024-04935-2129:3(1891-1947)Online publication date: 1-Mar-2024
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