Papers › Describing a Knowledge Base
Describing a Knowledge Base
Qingyun Wang, Xiaoman Pan, Lifu Huang, Boliang Zhang, Zhiying Jiang, Heng Ji, Kevin Knight
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new \emph{table position self-attention} to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% - 72.6% F-score.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| KB-to-Language Generation | Wikipedia Person and Animal Dataset | KB-to-Language Generation Model | BLEU | 23.2 | #1 of 1 | Archive leaderboard | report |
| KB-to-Language Generation | Wikipedia Person and Animal Dataset | KB-to-Language Generation Model | METEOR | 23.4 | #1 of 1 | Archive leaderboard | report |
| KB-to-Language Generation | Wikipedia Person and Animal Dataset | KB-to-Language Generation Model | ROUGE | 42.0 | #1 of 1 | Archive leaderboard | report |
| Table-to-Text Generation | Wikipedia Person and Animal Dataset | KB-to-Language Generation Model | BLEU | 23.2 | #2 of 2 | Archive leaderboard | report |
| Table-to-Text Generation | Wikipedia Person and Animal Dataset | KB-to-Language Generation Model | METEOR | 42.0 | #2 of 2 | Archive leaderboard | report |
| Table-to-Text Generation | Wikipedia Person and Animal Dataset | KB-to-Language Generation Model | ROUGE | 23.4 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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