# Generate FAIR Literature Surveys with Scholarly Knowledge Graphs

arXiv: https://arxiv.org/abs/2006.01747 · HTML: https://mlsanity.com/paper/2006.01747/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations](https://mlsanity.com/paper/2503.10658/) (2025)
- [From Papers to Panoramas: Building Hierarchies of Scientific Literature at Scale](https://mlsanity.com/paper/2504.13834/) (2025)
- [Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case](https://mlsanity.com/paper/2207.05188/) (2022)
- [Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph](https://mlsanity.com/paper/2305.12900/) (2023)
- [Clustering Semantic Predicates in the Open Research Knowledge Graph](https://mlsanity.com/paper/2210.02034/) (2022)
- [Toward Semantic Publishing in Non-Invasive Brain Stimulation: A Comprehensive Analysis of rTMS Studies](https://mlsanity.com/paper/2310.06517/) (2023)
- [Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence](https://mlsanity.com/paper/2601.05051/) (2026)

## 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.