Entity ID Naming Table for a Cross-Medicine Knowledge Graph on Type 2 Diabetes

Zekun Zhou published an entity ID naming table on 2026-05-13. The table is part of a study constructing a cross-medicine knowledge graph containing 245,235 entities and 7,155,373 triples to elucidate mechanisms of Type 2 Diabetes Mellitus. The 5.5 KB XLS file provides identifiers for entities integrated from sources like Hetionet, SymMap, TCMBank, STRING, and TTD.

Use Cases

• Mapping entity identifiers for integration into the described cross-medicine knowledge graph based on the multi-source biomedical data fusion.
• Supporting entity alignment tasks for the 15 entity types and 52 relation types mentioned in the study.
• Providing a reference for interpreting paths in the unified scoring framework that prioritizes drug candidates like Abelmoschus manihot and Topiramate.

Strengths

• The dataset is explicitly linked to a published methodology integrating data from five established biomedical sources (Hetionet, SymMap, TCMBank, STRING, TTD).
• The associated knowledge graph is large-scale, containing 245,235 entities and 7,155,373 triples covering 709 core T2DM genes.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (5.5 KB), indicating it is likely a lookup table rather than containing the primary graph data.

Provenance

Source

Zekun Zhou via figshare

Collection Method

Entity alignment and relation consolidation from multi-source biomedical data using Jaccard and overlap-based fusion strategies.

Freshness

Last updated 2026-05-13 17:33:19; freshness should be verified.