Generate FAIR Literature Surveys with Scholarly Knowledge Graphs
Создание соответствующих принципам FAIR обзоров литературы с использованием научных графов знаний
2020-08-01
SCID: 54.1/qph7k596
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FAIR Data PrinciplesFAIR literature surveysOpen Research Knowledge Graphresearch contribution comparisonscholarly knowledge graphs
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Abstract (AI)
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.
Key Findings
1
A methodology and system use scholarly knowledge graphs to compare research contributions by problems, materials, methods, and results.
2
Evaluation uses data extracted from published review articles and assesses the FAIRness of comparisons published through the ORKG.
3
The approach decomposes literature comparison into finding similar contributions, aligning descriptions, visualizing comparisons, and publishing results.
4
The methodology is implemented in the Open Research Knowledge Graph, enabling collaborative description, discovery, and comparison of research contributions.
5
The system supports rapid familiarization with research domains and publication of literature surveys following FAIR Data Principles.
Research Object
research contribution comparisons generated from scholarly literature using the Open Research Knowledge Graph (ORKG)
Research Subject
methodology and system for finding, aligning, visualizing, and publishing FAIR comparisons of research contributions, including their problems, materials, methods, and results
Publication Details
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2020-08-01
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References available in scid.ai5
The FAIR Guiding Principles for scientific data management and stewardship2016
Enriching Word Vectors with Subword Information2017
Analyzing the Past to Prepare for the Future: Writing a Literature Review2002
Open Research Knowledge Graph2019
Comparing research contributions in a scholarly knowledge graph2019