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.
@article{arxiv.2105.15033,
title = {DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction},
author = {Dejie Chang and Mosha Chen and Chaozhen Liu and Liping Liu and Dongdong Li and Wei Li and Fei Kong and Bangchang Liu and Xiaobin Luo and Ji Qi and Qiao Jin and Bin Xu},
journal= {arXiv preprint arXiv:2105.15033},
year = {2021}
}