# CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts

arXiv: https://arxiv.org/abs/2408.09070 · HTML: https://mlsanity.com/paper/2408.09070/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?](https://mlsanity.com/paper/2308.10168/) (2023)
- [Language Models of Code are Few-Shot Commonsense Learners](https://mlsanity.com/paper/2210.07128/) (2022)
- [At Which Training Stage Does Code Data Help LLMs Reasoning?](https://mlsanity.com/paper/2309.16298/) (2023)
- [If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents](https://mlsanity.com/paper/2401.00812/) (2024)
- [Code4Struct: Code Generation for Few-Shot Event Structure Prediction](https://mlsanity.com/paper/2210.12810/) (2022)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [Generative Knowledge Graph Construction: A Review](https://mlsanity.com/paper/2210.12714/) (2022)
- [KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction](https://mlsanity.com/paper/2403.07969/) (2024)
- [STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths](https://mlsanity.com/paper/2006.10217/) (2020)
- [Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings](https://mlsanity.com/paper/1906.02002/) (2019)
- [Are Large Language Models a Good Replacement of Taxonomies?](https://mlsanity.com/paper/2406.11131/) (2024)
- [TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks](https://mlsanity.com/paper/2403.09207/) (2024)
- [Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2101.11268/) (2021)
- [ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation](https://mlsanity.com/paper/2311.13258/) (2023)
- [Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy](https://mlsanity.com/paper/2403.04160/) (2024)
- [Graphine: A Dataset for Graph-aware Terminology Definition Generation](https://mlsanity.com/paper/2109.04018/) (2021)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [GIANT: Scalable Creation of a Web-scale Ontology](https://mlsanity.com/paper/2004.02118/) (2020)

## Built on by (most cited first, 10 of 10)

- [From Papers to Panoramas: Building Hierarchies of Scientific Literature at Scale](https://mlsanity.com/paper/2504.13834/) (2025)
- [Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion](https://mlsanity.com/paper/2505.13282/) (2025)
- [Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning](https://mlsanity.com/paper/2510.15191/) (2025)
- [Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims](https://mlsanity.com/paper/2506.10728/) (2025)
- [KnowCoder-V2: Deep Knowledge Analysis](https://mlsanity.com/paper/2506.06881/) (2025)
- [CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systems](https://mlsanity.com/paper/2601.05520/) (2026)
- [Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark](https://mlsanity.com/paper/2503.10357/) (2025)
- [Reference-Free Evaluation of Taxonomies](https://mlsanity.com/paper/2505.11470/) (2025)
- [CodeNER: Code Prompting for Named Entity Recognition](https://mlsanity.com/paper/2507.20423/) (2025)
- [Event Ontology Expansion via LLM-Based Conceptualization](https://mlsanity.com/paper/2606.21048/) (2026)

## Abstract (arXiv)

Taxonomies play a crucial role in various applications by providing a structural representation of knowledge. The task of taxonomy expansion involves integrating emerging concepts into existing taxonomies by identifying appropriate parent concepts for these new query concepts. Previous approaches typically relied on self-supervised methods that generate annotation data from existing taxonomies. However, these methods are less effective when the existing taxonomy is small (fewer than 100 entities). In this work, we introduce CodeTaxo, a novel approach that leverages large language models through code language prompts to capture the taxonomic structure. Extensive experiments on five real-world benchmarks from different domains demonstrate that CodeTaxo consistently achieves superior performance across all evaluation metrics, significantly outperforming previous state-of-the-art methods. The code and data are available at https://github.com/QingkaiZeng/CodeTaxo-Pub.