Transductive Knowledge Graph Completion Benchmark Results

9.5 KB of benchmark results for transductive knowledge graph completion models, including NBFNet and LMKE. The data was compiled by Qingsong Li, with results taken from original papers and some trained with original experimental settings. The dataset was last updated on March 19, 2026.

Use Cases

• Benchmarking knowledge graph completion models based on reported performance metrics.
• Comparing the effectiveness of structural models like NBFNet against other methods.
• Analyzing model performance on tasks like link prediction for knowledge graphs.

Strengths

• Dataset is small at 9.5 KB, enabling quick download and inspection.
• Results are clearly marked, with the best in bold and second-best underlined.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count and column definitions are unknown, requiring manual inspection after download.
• Description metadata is limited; actual data quality requires manual inspection after download.
• Some results are omitted due to unavailable source code, which may affect completeness.

Provenance

Source

figshare, author Qingsong Li.

Collection Method

Results compiled from original papers and some trained with original experimental settings.

Freshness

Last updated 2026-03-19 17:41:17; freshness should be verified.