Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion (2025)

Abstract: Taxonomies are hierarchical knowledge graphs crucial for recommendation systems, and web applications. As data grows, expanding taxonomies is essential, but existing methods face key challenges: (1) discriminative models struggle with representation limits and generalization, while (2) generative methods either process all candidates at once, introducing noise and exceeding context limits, or discard relevant entities by selecting noisy candidates. We propose LORex ( \textbf{L} ineage- \textbf{O} riented \textbf{Re} asoning for Taxonomy E \textbf{x} pansion), a plug-and-play framework that combines discriminative ranking and generative reasoning for efficient taxonomy expansion. Unlike prior methods, LORex ranks and chunks candidate terms into batches, filtering noise and iteratively refining selections by reasoning candidates’ hierarchy to ensure contextual efficiency. Extensive experiments across four benchmarks and twelve baselines show that LORex improves accuracy by 12% and Wu & Palmer similarity by 5% over state-of-the-art methods.

## introduction taxonomies are hierarchical graph structures that capture hypernymy ("is-a") relationships among concepts, making them essential for knowledge organization across various domains. conglomerates leverage them to power search engines @xcite , enhance product recommendations @xcite and improve advertisements @xcite . despite their utility, real-world taxonomies are manually created by domain experts, which is cumbersome and costly, limiting their ability to capture emerging concepts @xcite . additionally, as * corresponding author. new concepts continuously emerge, updating taxonomies manually becomes increasingly impractical. seed taxonomy expanded taxonomy 3 stage llm prompting llm prompting expanded taxonomy environment pollution noise pollution air pollution query term marine pollution choose the parent term from this liste d pollutiok d air pollutiok d environmenp d noise pollution water pollution filter find parent verify parent paths discriminative model generative model lorex rank terms chunk terms scoring function to overcome the limitations of manual taxonomy construction, research has now shifted toward taxonomy expansion, which involves the integration of new entities into an existing seed taxonomy @xcite . this process positions new concepts under appropriate existing nodes, known as anchor nodes, to preserve the structural integrity of the original taxonomy. as illustrated in fig. 1 , this ensures that new terms, like "marine pollution", are inserted under "water pollution" rather than the broader "pollution", maintaining hierarchical coherence. research on taxonomy expansion has evolved through three distinct generations. the firstgeneration approaches primarily rely on the seed taxonomy as weak supervision to assess hypernymy relationships, leveraging lexical pattern matching @xcite and distributional embeddings @xcite . however, these methods are inherently constrained by limited self-supervised annotation data and, therefore, fail to fully harness the hierarchical and structural intricacies embedded within taxonomies. second-generation research improves taxonomy expansion by incorporating structural summaries like mini-paths @xcite , paths @xcite , random walks @xcite , ego-nets @xcite , and local graphs @xcite to enrich hierarchical representation. these methods are also supplemented with external knowledge, such as domainspecific corpora @xcite and concept definitions @xcite . however, they struggle with small-scale taxonomies, where limited selfsupervised data hinders model training and generalization, requiring more scalable approaches. the current generation research on taxonomy expansion leverages llms to integrate new concepts into existing taxonomies. models like llama @xcite and @xcite , excel in structural knowledge comprehension @xcite . notably, gpt-4, with 1.7 trillion parameters, generates parent terms directly @xcite , while llama-2 (7b parameters) requires fine-tuning on self-supervised annotation data for taxonomy expansion @xcite . while effective, these approaches incur high computational costs. fine-tuning llama models requires substantial resources, whereas gpt-4 inference is expensive due to its massive parameter size and paid api-based access, further increasing costs and reducing flexibility for scaling taxonomy expansion. additionally, llm-driven methods face context size limitations -some attempt to encode all candidates into prompts @xcite , which is impractical, while others rely on top-k selection @xcite , risking the omission of relevant terms. to address limitations of existing taxonomy expansion methods, we propose lorex (lineage-oriented reasoning for taxonomy expansion), a plug-and-play that integrates discriminative ranking and generative reasoning to systematically expand taxonomies without fine-tuning llms or discarding relevant candidates. inspired by @xcite , lorex first ranks the candidate terms for an entity using a discriminative ranker, named tempora, which leverages lineage structures to score and rank candidates. tempora converts the lineage into a taxonomy path via an euler tour, then verbalizes relationships by labeling edges as "parent of" and "child of". ranked candidates are then chunked into batches, where an llm first filters relevant batches and performs hierarchical reasoning to select the best parent. to verify the correctness, we introduce an llm-based path-scoring function that ranks candidates based on their lineage structure. we conduct extensive experiments to evaluate tempora for candidate ranking and lorex for taxonomy expansion on four public benchmark datasets, including semeval-2016 task 13 taxonomy extraction evaluation @xcite and wordnet @xcite . tempora outperforms all ranking baselines, improving hit@k by 21.3%, despite their extensive self-supervised training. lorex surpasses eight baselines across four benchmarks, achieving 12% higher accuracy and a 5% gain in wu & palmer (wu&p) metric. furthermore, we perform ablations to analyze the performance of lorex under varying conditions, including its effectiveness with different open-source llms. we summarize our main contributions below: ## problem definition definition 1 taxonomy: a taxonomy as new entities continue to emerge, the key challenge is incorporating them into an existing taxonomy t o , referred to as taxonomy expansion, definition 2 taxonomy expansion: taxonomy expansion is the process of incorporating emergent concepts c into a seed taxonomy t @xmath0, e o ) to create an updated taxonomy @xmath1, e o ∪ r), where r is the set of new relations connecting existing entities e o with the new concepts c. ## proposed method lorex, a plug-and-play taxonomy expansion framework (fig. 2 ), integrates discriminative ranking (tempora) with iterative structure reasoning to integrate new concepts into an existing taxonomy. ## conclusion we introduce lorex, a plug-and-play taxonomy expansion framework which integrates discriminative ranking and generative reasoning to effectively retrieve and verify parent-child relationships without requiring fine-tuning of large language models. by leveraging the discriminative ranker tempora, the framework ranks candidate terms and optimally chunks them for llm processing, ensuring relevant candidates are retained while maintaining context length constraints. extensive experiments highlight that lorex outperforms the best baseline. ablation studies highlight the importance of hierarchical reasoning, discriminative ranking, and verification strategies, which improve retrieval quality. case studies further validate lorex's effectiveness in resolving ambiguous classifications and reinforcing hierarchical consistency.