# Creating a Scholarly Knowledge Graph from Survey Article Tables

arXiv: https://arxiv.org/abs/2012.00456 · HTML: https://mlsanity.com/paper/2012.00456/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Towards Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories](https://mlsanity.com/paper/2308.01732/) (2023)
- [Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment](https://mlsanity.com/paper/2405.02321/) (2024)
- [Benchmarking Large Language Models for Knowledge Graph Validation](https://mlsanity.com/paper/2602.10748/) (2026)
- [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)

Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2,626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.