mlsanityData to 7 Oct 2026

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

arXiv 2408.09070 ↗First posted 17 Aug 2024cs.CL cs.IRcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 17 Aug 2024.

Builds on

24 of 46 references.

References · 24

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  10. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
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12 more
  1. 2022Generative Knowledge Graph Construction: A Reviewcited by 49
  2. 2024KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extractioncited by 33
  3. 2020STEAM: Self-Supervised Taxonomy Expansion with Mini-Pathscited by 31
  4. 2019Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddingscited by 23
  5. 2024Are Large Language Models a Good Replacement of Taxonomies?cited by 19
  6. 2024TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Taskscited by 14
  7. 2021Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansioncited by 13
  8. 2023ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representationcited by 13
  9. 2024Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomycited by 11
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  11. 2024A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansioncited by 7
  12. 2020GIANT: Scalable Creation of a Web-scale Ontologycited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 2.

10 papers.

  1. 2025From Papers to Panoramas: Building Hierarchies of Scientific Literature at Scalecited by 7
  2. 2025Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansioncited by 5
  3. 2025Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learningcited by 5
  4. 2025Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claimscited by 3
  5. 2025KnowCoder-V2: Deep Knowledge Analysiscited by 1
  6. 2026CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systemscited by 1
  7. 2025Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmarkcited by 0
  8. 2025Reference-Free Evaluation of Taxonomiescited by 0
  9. 2025CodeNER: Code Prompting for Named Entity Recognitioncited by 0
  10. 2026Event Ontology Expansion via LLM-Based Conceptualizationcited by 0