# TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters

arXiv: https://arxiv.org/abs/2201.06771 · HTML: https://mlsanity.com/paper/2201.06771/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 26 of 26)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://mlsanity.com/paper/2004.06100/) (2020)
- [Hyperbolic Entailment Cones for Learning Hierarchical Embeddings](https://mlsanity.com/paper/1804.01882/) (2018)
- [Order-Embeddings of Images and Language](https://mlsanity.com/paper/1511.06361/) (2015)
- [Word Representations via Gaussian Embedding](https://mlsanity.com/paper/1412.6623/) (2014)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering](https://mlsanity.com/paper/1812.09551/) (2018)
- [Spherical Text Embedding](https://mlsanity.com/paper/1911.01196/) (2019)
- [Weakly-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/1812.11270/) (2018)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (2020)
- [Text Classification Using Label Names Only: A Language Model Self-Training Approach](https://mlsanity.com/paper/2010.07245/) (2020)
- [HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion](https://mlsanity.com/paper/1910.08194/) (2019)
- [STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths](https://mlsanity.com/paper/2006.10217/) (2020)
- [Discriminative Topic Mining via Category-Name Guided Text Embedding](https://mlsanity.com/paper/1908.07162/) (2019)
- [SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble](https://mlsanity.com/paper/1910.08192/) (2019)
- [DATE: Detecting Anomalies in Text via Self-Supervision of Transformers](https://mlsanity.com/paper/2104.05591/) (2021)
- [Octet: Online Catalog Taxonomy Enrichment with Self-Supervision](https://mlsanity.com/paper/2006.10276/) (2020)
- [Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding](https://mlsanity.com/paper/2007.09536/) (2020)
- [CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring](https://mlsanity.com/paper/2010.06714/) (2020)
- [Multi-Class Data Description for Out-of-distribution Detection](https://mlsanity.com/paper/2104.00941/) (2021)
- [Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations](https://mlsanity.com/paper/2106.02974/) (2021)
- [Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding](https://mlsanity.com/paper/2010.06705/) (2020)
- [Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings](https://mlsanity.com/paper/1803.03370/) (2018)
- [Out-of-Category Document Identification Using Target-Category Names as Weak Supervision](https://mlsanity.com/paper/2111.12796/) (2021)

## Built on by (most cited first, 30 of 30)

- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora](https://mlsanity.com/paper/2506.10737/) (2025)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Taxonomy-guided Semantic Indexing for Academic Paper Search](https://mlsanity.com/paper/2410.19218/) (2024)
- [Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy](https://mlsanity.com/paper/2403.04160/) (2024)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation](https://mlsanity.com/paper/2211.01981/) (2022)
- [PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://mlsanity.com/paper/2302.05550/) (2023)
- [Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation](https://mlsanity.com/paper/2502.11181/) (2025)
- [Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding](https://mlsanity.com/paper/2304.04099/) (2023)
- [SCStory: Self-supervised and Continual Online Story Discovery](https://mlsanity.com/paper/2312.03725/) (2023)
- [Self-supervised Topic Taxonomy Discovery in the Box Embedding Space](https://mlsanity.com/paper/2408.15050/) (2024)
- [Topic Coverage-based Demonstration Retrieval for In-Context Learning](https://mlsanity.com/paper/2509.12451/) (2025)
- [Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration](https://mlsanity.com/paper/2509.15786/) (2025)
- [TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2601.09633/) (2026)
- [Seeded Hierarchical Clustering for Expert-Crafted Taxonomies](https://mlsanity.com/paper/2205.11602/) (2022)
- [Using Zero-shot Prompting in the Automatic Creation and Expansion of Topic Taxonomies for Tagging Retail Banking Transactions](https://mlsanity.com/paper/2401.06790/) (2024)
- [CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systems](https://mlsanity.com/paper/2601.05520/) (2026)
- [EvoTaxo: Building and Evolving Taxonomy from Social Media Streams](https://mlsanity.com/paper/2603.19711/) (2026)
- [SCALE: Scientific Concept Aggregation via LLMs and Embeddings for Fine-Grained Taxonomy Extension](https://mlsanity.com/paper/2608.07254/) (2026)
- [Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion](https://mlsanity.com/paper/2308.11720/) (2023)
- [Hierarchical Multi-Label Generation with Probabilistic Level-Constraint](https://mlsanity.com/paper/2505.03775/) (2025)
- [Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics](https://mlsanity.com/paper/2505.16493/) (2025)
- [Improving Scientific Document Retrieval with Academic Concept Index](https://mlsanity.com/paper/2601.00567/) (2026)
- [SciDef: Datasets and Tools for Automated Definition Extraction from Scientific Literature with LLMs](https://mlsanity.com/paper/2602.05413/) (2026)
- [Term-Centric Hierarchy Induction from Heterogeneous Corpora](https://mlsanity.com/paper/2606.26963/) (2026)
- [An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation](https://mlsanity.com/paper/2608.07023/) (2026)
- [Security and Privacy Taxonomy Generation from Mobile App Reviews](https://mlsanity.com/paper/2608.09049/) (2026)
- [TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale](https://mlsanity.com/paper/2608.30614/) (2026)
- [Towards Automatic Evolution Tree Generation from Citation Graphs](https://mlsanity.com/paper/2609.09561/) (2026)

## Abstract (arXiv)

Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with each other: (i) locally discriminative embedding optimizes the text embedding space to be discriminative among known (i.e., given) sub-topics, and (ii) novelty adaptive clustering assigns terms into either one of the known sub-topics or novel sub-topics. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage but also outperforms all other baselines for a downstream task.