BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration

Yancheng Ling; Zhenlin Qin; Leizhen Wang; Zhenliang Ma

cs.CLApr 3, 2026

BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration

Authors: Yancheng Ling, Zhenlin Qin, Leizhen Wang, Zhenliang Ma

Organizations: KTH Royal Institute of Technology, 11428 Sweden · Monash University, Clayton, Australia, 3800

Abstract

Taxonomy induction is crucial for organizing concepts into explicit and interpretable semantic hierarchies. While existing methods have achieved promising results, their generalization, structural reliability, and efficiency remain limited, hindering their performance in zero-shot and large-scale scenarios. To overcome these limitations, we introduce BoostTaxo, a boosting-style LLM framework for zero-shot taxonomy induction. It takes a set of domain terms as inputs and performs parent identification in a coarse-to-fine manner, employing retrieval-augmented definition refinement, hybrid parent candidate selection, candidate rating, and structure-aware score calibration to improve taxonomy construction. Specifically, a lightweight LLM is used to efficiently filter candidate parents, while a large-scale LLM is employed to rank and score candidate parents for fine-grained parent selection. Structural features are further incorporated to calibrate candidate edge weights and enhance the reliability of the induced taxonomy. The unified BoostTaxo is evaluated on three public benchmark datasets, namely WordNet, DBLP, and SemEval-Sci, and achieves superior or comparable performance to state-of-the-art methods in zero-shot taxonomy induction. The ablation study validates the contribution of the hybrid parent candidate selection and the structure-aware score calibration to the overall performance. Further analysis investigates the impact of candidate selection size on taxonomy quality and presents representative case and failure studies, providing deeper insights into the effectiveness and limitations of the proposed framework.

Figures & tables

Fig. 1: Boosting-Style Zero-Shot Taxonomy Induction with Large Language Models

Fig. 2: The boosting-style zero-shot framework for taxonomy induction. Starting from a root concept and a term set, the framework first enriches term semantics through retrieval-augmented definitions, which combine Wikipedia retrieval with definition refinement. Then, for each term, it performs parent candidate selection in three stages. First, Hybrid Parent Candidate Selection identifies a set of potential parents by jointly considering Is-A judgment and definition matching. Second, Candidate Ranking and Scoring ranks these candidates and assigns an initial score to each of them. Third, Calibration with Structural Features further adjusts these scores to improve their structural consistency and reliability. After obtaining parent candidates and calibrated scores for all terms, the final taxonomy is constructed by applying a maximum spanning arborescence algorithm.

Fig. 3: Prompt design for LLM-Based term definition refinement

Fig. 4: Prompt design for LLM-based candidate parent ranking and scoring

Fig. 5: Prompt design for LLM-Based calibration of candidate parent scores

Input: Candidate edges

E

, root node

r

, threshold

\tau_{m}

, LLM

Output: Adjusted candidate edges

E^{\prime}

1. Filter mutual edges in

E

with threshold

\tau_{m}

to obtain the filtered edge set

E_{f}

2. Normalize the scores of edges in

E_{f}

to obtain

s(t,p)\in[0,1]

3. Build candidate mappings

P(t)

and the parent-to-children graph from

E_{f}

4. For each edge

(t,p)\in E_{f}

, compute

\mathrm{margin}(t,p)

\mathrm{pop}(p)

\mathrm{pb}(t,p)

\mathrm{skip\_support}(t,p)

\mathrm{coh}(t,p)

, and

\mathrm{depth\_pen}(p)

5. For each child term

t

, query the LLM with all candidate parents in

P(t)

and obtain

\mathrm{penalty}_{\mathrm{eff}}(t,p)\in[0,0.5]

for each

p\in P(t)

TABLE I: LLM-based score calibration algorithm

Model	WordNet						DBLP						SemEval-Sci
Model	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$
Supervised Fine-tuning
Graph2Taxo	79.20	47.80	59.60	75.60	37.00	49.70	47.85	30.23	37.05	46.63	28.49	35.37	82.45	36.15	50.27	79.37	34.52	46.87
CTP	69.30	66.20	66.70	53.30	49.80	51.50	45.62	41.39	43.40	38.21	33.73	35.83	52.41	33.88	41.16	31.18	29.42	30.27
CTP-Llama-2-7B	73.48	70.02	71.71	55.42	51.98	53.64	48.73	39.88	43.86	44.39	35.81	39.64	61.98	54.09	57.77	48.33	41.92	44.90
5-shot Setting

TABLE II: Performance comparison on WordNet, DBLP, and SemEval-Sci datasets.

Setting	WordNet						DBLP						SemEval-Sci
Setting	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$
w/o HPCS	71.60	82.06	76.47	57.38	57.28	57.33	34.27	84.74	48.80	45.66	45.66	45.66	90.28	60.41	72.39	54.08	53.98	54.03
w/ HPCS	79.00	80.65	79.81	61.21	61.19	61.20	39.55	85.41	54.07	43.21	43.21	43.21	93.88	65.76	77.34	57.88	57.88	57.88

TABLE III: Ablation study on the effect of Hybrid Parent Candidate Selection across WordNet, DBLP, and SemEval-Sci datasets.

Setting	WordNet						DBLP						SemEval-Sci
Setting	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$
w/o LSC-SF	79.00	80.65	79.81	61.21	61.19	61.20	39.55	85.41	54.07	43.21	43.21	43.21	93.88	65.76	77.34	57.88	57.88	57.88
w/ LSC-SF	84.29	81.34	82.79	63.57	63.43	63.50	59.02	80.81	68.21	48.55	48.55	48.55	88.11	70.60	78.39	60.88	60.88	60.88

TABLE IV: Ablation study on the effect of LLM-based Score Calibration with Structural Features across WordNet, DBLP, and SemEval-Sci datasets.

Setting	WordNet						DBLP						SemEval-Sci
Setting	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$
$top_{k2}=1$	82.55	82.75	82.65	63.71	63.66	63.69	59.86	76.66	67.22	40.53	40.53	40.53	90.59	63.59	74.73	56.64	56.64	56.64
$top_{k2}=3$	79.00	80.65	79.81	61.21	61.19	61.20	39.55	85.41	54.07	43.21	43.21	43.21	93.88	65.76	77.34	57.88	57.88	57.88
$top_{k2}=5$	79.17	79.98	79.57	60.72	60.69	60.71	33.28	85.86	47.96	43.43	43.43	43.43	94.41	63.89	76.21	57.35	57.35	57.35

TABLE V: Ablation study on the choice of

top_{k2}

across WordNet, DBLP, and SemEval-Sci datasets.

Model	WordNet						DBLP						SemEval-Sci
Model	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$	$P_{a}$	$R_{a}$	$F1_{a}$	$P_{e}$	$R_{e}$	$F1_{e}$
Qwen3-14B	85.30	75.04	79.84	55.46	55.21	55.34	51.83	76.21	61.70	39.73	39.64	39.69	85.38	65.36	74.04	53.37	53.27	53.32
Qwen3-30B	77.10	80.27	78.65	55.77	55.62	55.69	53.72	79.46	64.10	46.41	46.10	46.26	76.57	70.85	73.60	57.35	57.35	57.35
GPT-4mini	78.82	70.27	74.30	46.56	46.27	46.42	53.78	78.23	63.74	42.32	42.32	42.32	91.56	63.44	74.95	47.43	47.43	47.43
GPT-4o	84.29	81.34	82.79	63.57	63.43	63.50	59.02	80.81	68.21	48.55	48.55	48.55	88.11	70.60	78.39	60.88	60.88	60.88

TABLE VI: Ablation study results across WordNet, DBLP, and SemEval-Sci datasets using Micro average scores.

Fig. 6: Comparison of taxonomy construction results across different methods.(a) is the ground truth taxonomy, and (b)–(d) show the taxonomies generated by GPT-4o using CoL five-shot, CoL zero-shot, and BoostTaxo zero-shot, respectively. Green indicates correct nodes and edges, while orange indicates incorrect ones. Among the three settings, BoostTaxo zero-shot yields the structure that is closest to the ground truth.

Fig. 7: Representative failure cases in taxonomy construction. In each subfigure, the left side shows the ground-truth taxonomy and the right side shows the predicted taxonomy. Green indicates correct terms and edges, while orange indicates incorrect ones. (a) shows hierarchy flattening, where intermediate levels are removed and descendants are directly attached to higher-level terms. (b) shows excessive hierarchy depth, where extra intermediate terms are incorrectly introduced. (c) shows sibling node confusion, where a term is attached to an incorrect sibling branch. (d) shows cross-branch misattachment, where a term is assigned to the wrong semantic branch.

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SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging

Yirui Zhang, Yixuan Tang, Yandong Sun, Mong-Li Lee+1

School of Computing, National University of Singapore

Spectral Clustering Taxonomy Induction

Open paper AskPDF arXiv

Taxonomy induction aims to organize concept sets into coherent hierarchical structures. Recent LLM-based methods can induce taxonomies directly from flat term lists, avoiding the need for corpora, but degrade sharply as concept sets scale up. We argue that this degradation stems not only from context length limitations, but also from structural failures in hierarchical reasoning. To address this, we adopt a divide-and-merge paradigm that partitions concepts into smaller subsets, induces local taxonomies, and merges them into a global hierarchy. However, we identify two structural failure modes inherent to this paradigm: Structural Fragmentation, where partitioning weakens local hierarchical signals, and Parent Displacement, where locally plausible relations are misplaced in the global hierarchy. To address both, we propose SPARROW, a scalable taxonomy induction framework that combines structure-preserving spectral partitioning to retain hierarchical connectivity within each block, and constraint-guided incremental fusion that treats block-level relations as structural constraints rather than ground truth for global placement. Experiments on large-scale benchmarks show that SPARROW consistently achieves the strongest global structural quality across backbones. The code is available at https://github.com/rebeccazyr/SPARROW.

Abstract

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