Generate FAIR Literature Surveys with Scholarly Knowledge Graphs
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Abstract (arXiv)
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 facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2020.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2025LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitationscited by 12
- 2025From Papers to Panoramas: Building Hierarchies of Scientific Literature at Scalecited by 7
- 2022Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Casecited by 2
- 2023Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graphcited by 2
- 2022Clustering Semantic Predicates in the Open Research Knowledge Graphcited by 1
- 2023Toward Semantic Publishing in Non-Invasive Brain Stimulation: A Comprehensive Analysis of rTMS Studiescited by 0
- 2026Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligencecited by 0
- 2026Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence8 Jan 2026
- 2025From Papers to Panoramas: Building Hierarchies of Scientific Literature at Scale18 Apr 2025
- 2025LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations8 Mar 2025
- 2023Toward Semantic Publishing in Non-Invasive Brain Stimulation: A Comprehensive Analysis of rTMS Studies10 Oct 2023
- 2023Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph22 May 2023
- 2022Clustering Semantic Predicates in the Open Research Knowledge Graph5 Oct 2022
- 2022Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case11 Jul 2022