Taxonomy Induction

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20 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Peeling the Matryoshka: Extracting Ontological Structure from Retrieval Embeddings
Oct 30, 2026Eunchan Kim, Inwhee JoeTaxonomy Induction Hierarchical Representation Learning
Hanyang University
Embedding-Based Educational Taxonomies
Oct 24, 2026Kia Karbasi, Kaveh Karbasi, Gregory PottieTaxonomy Induction Semantic Clustering
University of California, Los Angeles · Tempus AI, Inc.
InsightRAG: Aggregating Distributed Scholarly Judgments for Scientific Survey Generation
Oct 20, 2026Yuquan Lin, Wenshuo Zhang, Yilin Ye +4Retrieval-Augmented Generation Evidence Retrieval
The Hong Kong University of Science and Technology · The Hong Kong University of Science and Technology (Guangzhou)
Uncovering Semantic Hierarchies in Text-Attributed Graphs via Variational EM-based LLM-GNN Synergy
Oct 17, 2026Yunhui Liu, Xudong Jin, Qizhuo Xie +5Text-Attributed Graph Learning Hierarchical Representation Learning
State Key Laboratory for Novel Software Technology, Nanjing University · Department of Statistics, University of Wisconsin–Madison · National Engineering Laboratory for Big Data Analysis and Applications, Peking University
From Plausible Hierarchies to Useful Taxonomies: Evaluating Agentic Harnesses on Customer Feedback
Oct 7, 2026Prabhath Chellingi, Raviraja G, Viraj BagalTaxonomy Induction Automated Evaluation
Enterpret
Towards Automatic Evolution Tree Generation from Citation Graphs
Sep 9, 2026Zexing Zhao, Yuntong Hu, Liang ZhaoCitation Network Analysis Bibliometric Analysis
School of Mechanical Engineering, Georgia Institute of Technology · Department of Computer Science, Emory University
SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging
Sep 7, 2026Yirui Zhang, Yixuan Tang, Yandong Sun +2Spectral Clustering Taxonomy Induction
School of Computing, National University of Singapore
Autoresearch for Marketplace Catalogs: From Legacy Forms to AI-Native Matching
Aug 31, 2026Kartik Ravisankar, Hojat Abdolanezhad, Daniel Capo +3User Preference Modeling Generative AI Evaluation
Thumbtack · San Francisco, CA, USA
TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale
Aug 31, 2026Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit PyatiTopic Modeling Semantic Mapping
Amazon
Security and Privacy Taxonomy Generation from Mobile App Reviews
Aug 10, 2026Moghis Fereidouni, Krishna Upadhyay, Umar Farooq +1Cybersecurity Taxonomy Induction
University of Kentucky · Louisiana State University
Fantastic Adaptive Taxonomies and How to Use Them
Jul 17, 2026Mert Cemri, Andrei Cojocaru, Melissa Pan +9Agent Failure Analysis LLM Agent Evaluation
University of California, Berkeley · Apple · Bespoke Labs
Term-Centric Hierarchy Induction from Heterogeneous Corpora
Jun 25, 2026Elena Senger, Yuri Campbell, Jan-Peter Bergmann +2Clustering Hierarchical Clustering
1MaiNLP, Center for Information and Language Processing, LMU Munich, Germany · 2Fraunhofer Institute for Systems and Innovation Research ISI, Germany · Department of Computer Science, IT University of Copenhagen, Denmark
ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems
Jun 19, 2026Junyi Lu, Mengyao Lyu, Jiahui Wu +6Software Engineering Hierarchical Classification
Institute of Software, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Nanyang Technological University, Singapore +2
TaBIIC2: Interactive Building of Ontological Taxonomies using Weighted Self-Organizing Maps
May 24, 2026Mathieu d'AquinSelf-Organizing Maps Ontology Learning
MosAIK Team, LORIA,MayCNRS,2026Universit´e de Lorraine
Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
May 20, 2026Stephen Meisenbacher, Peter NorlanderSemantic Mapping Taxonomy Induction
Technical University of Munich Garching, Germany · Loyola University Chicago Chicago, IL, USA
SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models
May 1, 2026Shiqiang Cai, Nianhong Niu, Shizhu He +2Hierarchical Representation Learning Taxonomy Induction
Institute of Automation, Chinese Academy of Sciences, Beijing, China · University of Science and Technology Beijing, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration
Apr 3, 2026Yancheng Ling, Zhenlin Qin, Leizhen Wang +1Taxonomy Induction Zero-Shot Learning
KTH Royal Institute of Technology, 11428 Sweden · Monash University, Clayton, Australia, 3800
EvoTaxo: Building and Evolving Taxonomy from Social Media Streams
Mar 20, 2026Yiyang Li, Tianyi Ma, Yanfang YeTaxonomy Induction Hierarchical Classification
University of Notre Dame
Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning
Mar 9, 2026Yunhui Liu, Yongchao Liu, Yinfeng Chen +3Taxonomy Induction Hierarchical Representation Learning
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Ant Group, Hangzhou, China · Ant Group, Beijing, China
TaxoBench: Can Deep Research Agents Retrieve and Organize? Evaluating the Synthesis Gap with Expert Taxonomies
Jan 18, 2026Ming Zhang, Jiabao Zhuang, Wenqing Jing +15AI Agent Benchmarks Deep Research Agents
Fudan University · Hunyuan Team, Tencent

Oct 30, 2026J/K move · Enter open · S save

Peeling the Matryoshka: Extracting Ontological Structure from Retrieval Embeddings

Eunchan Kim, Inwhee Joe

Hanyang University

Taxonomy Induction Hierarchical Representation Learning Text Embedding Models Matryoshka Representation Learning

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Taxonomy construction is essential for knowledge-intensive NLP yet remains costly: clustering-based methods (i) need careful per-corpus tuning and yield unstable hierarchies, while LLM-based pipelines (ii) depend on expensive task-specific training that does not transfer across domains. We report an empirical finding that bears on both: in Matryoshka Representation Learning (MRL) embeddings, where each leading prefix is itself a usable, coarser vector, prefix length aligns with the coarse-to-fine granularity of gold taxonomies; shuffled- and reverse-dimension controls isolate the alignment to the learned early prefix dimensions. The pattern holds across three architecturally distinct MRL models. To test whether this regularity is usable, we introduce PEEL (Peeling Embeddings to Extract Latent ontologies): a minimal probe over these frozen embeddings, trained once with an order-embedding objective that represents hierarchy as vector containment; a minimum spanning arborescence then decodes taxonomic DAGs. Using only a concept vocabulary (no documents), PEEL recovers taxonomic DAGs with high structural fidelity on two OLLM benchmarks, transferring to a new domain with no target-edge supervision. The end-to-end OLLM pipeline, which jointly discovers concepts and induces structure from document corpora, is reported as a cross-task reference. Pre-trained retrieval embeddings encode a decodable granularity gradient; a minimal probe renders it as explicit parent-child (is-a) structure.