# Describing a Knowledge Base

arXiv: https://arxiv.org/abs/1809.01797 · HTML: https://mlsanity.com/paper/1809.01797/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 18 of 18)

- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View](https://mlsanity.com/paper/2102.08585/) (2021)
- [Relational Memory Augmented Language Models](https://mlsanity.com/paper/2201.09680/) (2022)
- [Logic-Consistency Text Generation from Semantic Parses](https://mlsanity.com/paper/2108.00577/) (2021)
- [Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation](https://mlsanity.com/paper/2105.14778/) (2021)
- [Learning to Reason for Text Generation from Scientific Tables](https://mlsanity.com/paper/2104.08296/) (2021)
- [EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2111.00276/) (2021)
- [ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models](https://mlsanity.com/paper/2210.04325/) (2022)
- [Stage-wise Fine-tuning for Graph-to-Text Generation](https://mlsanity.com/paper/2105.08021/) (2021)
- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots](https://mlsanity.com/paper/2203.00732/) (2022)
- [Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation](https://mlsanity.com/paper/2210.12367/) (2022)
- [Plan-then-Seam: Towards Efficient Table-to-Text Generation](https://mlsanity.com/paper/2302.05138/) (2023)
- [An evaluation of template and ML-based generation of user-readable text from a knowledge graph](https://mlsanity.com/paper/2106.14613/) (2021)
- [Self-training from Self-memory in Data-to-text Generation](https://mlsanity.com/paper/2401.10567/) (2024)
- [Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models](https://mlsanity.com/paper/2505.06107/) (2025)
- [Data Transformation Strategies to Remove Heterogeneity](https://mlsanity.com/paper/2507.12677/) (2025)

## Abstract (arXiv)

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 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.