# DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction

arXiv: https://arxiv.org/abs/2105.15033 · HTML: https://mlsanity.com/paper/2105.15033/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 3 of 3)

- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)

## Built on by (most cited first, 5 of 5)

- [Exploring Partial Knowledge Base Inference in Biomedical Entity Linking](https://mlsanity.com/paper/2303.10330/) (2023)
- [Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management](https://mlsanity.com/paper/2409.13191/) (2024)
- [Mutually Guided Few-shot Learning for Relational Triple Extraction](https://mlsanity.com/paper/2306.13310/) (2023)
- [DREaM: Drug-Drug Relation Extraction via Transfer Learning Method](https://mlsanity.com/paper/2510.23189/) (2025)
- [DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data](https://mlsanity.com/paper/2607.09348/) (2026)

## Abstract (arXiv)

Knowledge Graph has been proven effective in modeling structured information and conceptual knowledge, especially in the medical domain. However, the lack of high-quality annotated corpora remains a crucial problem for advancing the research and applications on this task. In order to accelerate the research for domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a high-quality Chinese dataset for Diabetes knowledge graph, which contains 22,050 entities and 6,890 relations in total. We implement recent typical methods for Named Entity Recognition and Relation Extraction as a benchmark to evaluate the proposed dataset thoroughly. Empirical results show that the DiaKG is challenging for most existing methods and further analysis is conducted to discuss future research direction for improvements. We hope the release of this dataset can assist the construction of diabetes knowledge graphs and facilitate AI-based applications.