# A Knowledge Graph-Based Method for Automating Systematic Literature Reviews

arXiv: https://arxiv.org/abs/2208.02334 · HTML: https://mlsanity.com/paper/2208.02334/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2022.

## Built on by (most cited first, 2 of 2)

- [Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment](https://mlsanity.com/paper/2405.02321/) (2024)
- [Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery](https://mlsanity.com/paper/2509.10087/) (2025)

## Abstract (arXiv)

Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently screen and assess relevant publications. Another challenge is the continuous assessment of related work over a long period of time and the consequent need for a continuous update, which can be a time-consuming task. Knowledge graphs model entities in a connected manner and enable new insights using different reasoning and analysis methods. The objective of this work is to present an approach to partially automate the conduction of a Systematic Literature Review as well as classify and visualize the results as a knowledge graph. The designed software prototype was used for the conduction of a review on context-awareness in automation systems with considerably accurate results compared to a manual conduction.