# CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction

arXiv: https://arxiv.org/abs/1902.10326 · HTML: https://mlsanity.com/paper/1902.10326/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation](https://mlsanity.com/paper/2303.14480/) (2023)
- [Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances](https://mlsanity.com/paper/2310.16790/) (2023)
- [Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models](https://mlsanity.com/paper/2406.10902/) (2024)

## Abstract (arXiv)

Taxonomies play an important role in machine intelligence. However, most well-known taxonomies are in English, and non-English taxonomies, especially Chinese ones, are still very rare. In this paper, we focus on automatic Chinese taxonomy construction and propose an effective generation and verification framework to build a large-scale and high-quality Chinese taxonomy. In the generation module, we extract isA relations from multiple sources of Chinese encyclopedia, which ensures the coverage. To further improve the precision of taxonomy, we apply three heuristic approaches in verification module. As a result, we construct the largest Chinese taxonomy with high precision about 95% called CN-Probase. Our taxonomy has been deployed on Aliyun, with over 82 million API calls in six months.