  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.
