# Taxonomy Completion via Triplet Matching Network

arXiv: https://arxiv.org/abs/2101.01896 · HTML: https://mlsanity.com/paper/2101.01896/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 53 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [Self-Supervised Generalisation with Meta Auxiliary Learning](https://mlsanity.com/paper/1901.08933/) (2019)
- [Learning Longer-term Dependencies in RNNs with Auxiliary Losses](https://mlsanity.com/paper/1803.00144/) (2018)
- [Hierarchical Text Classification with Reinforced Label Assignment](https://mlsanity.com/paper/1908.10419/) (2019)
- [Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs](https://mlsanity.com/paper/2007.08294/) (2020)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (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)
- [Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition](https://mlsanity.com/paper/1704.01631/) (2017)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings](https://mlsanity.com/paper/1906.02002/) (2019)
- [Octet: Online Catalog Taxonomy Enrichment with Self-Supervision](https://mlsanity.com/paper/2006.10276/) (2020)
- [End-to-End Reinforcement Learning for Automatic Taxonomy Induction](https://mlsanity.com/paper/1805.04044/) (2018)
- [MetaPAD: Meta Pattern Discovery from Massive Text Corpora](https://mlsanity.com/paper/1703.04213/) (2017)
- [A User-Centered Concept Mining System for Query and Document Understanding at Tencent](https://mlsanity.com/paper/1905.08487/) (2019)
- [Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors](https://mlsanity.com/paper/2001.04803/) (2020)
- [Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach](https://mlsanity.com/paper/1804.10877/) (2018)
- [Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering](https://mlsanity.com/paper/1809.05618/) (2018)

## Built on by (most cited first, 21 of 21)

- [TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations](https://mlsanity.com/paper/2202.04887/) (2022)
- [Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations](https://mlsanity.com/paper/2106.02974/) (2021)
- [HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning](https://mlsanity.com/paper/2109.10500/) (2021)
- [TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks](https://mlsanity.com/paper/2403.09207/) (2024)
- [Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph](https://mlsanity.com/paper/2209.15214/) (2022)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement](https://mlsanity.com/paper/2306.14704/) (2023)
- [Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion](https://mlsanity.com/paper/2104.03682/) (2021)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [Expanding Knowledge Graphs with Humans in the Loop](https://mlsanity.com/paper/2212.05189/) (2022)
- [A Language Model based Framework for New Concept Placement in Ontologies](https://mlsanity.com/paper/2402.17897/) (2024)
- [Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model](https://mlsanity.com/paper/2406.17739/) (2024)
- [TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2601.09633/) (2026)
- [GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation](https://mlsanity.com/paper/2303.14480/) (2023)
- [Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning](https://mlsanity.com/paper/2605.00265/) (2026)
- [DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space](https://mlsanity.com/paper/2302.11165/) (2023)
- [Towards Visual Taxonomy Expansion](https://mlsanity.com/paper/2309.06105/) (2023)
- [Hierarchical Multi-Label Generation with Probabilistic Level-Constraint](https://mlsanity.com/paper/2505.03775/) (2025)
- [Reference-Free Evaluation of Taxonomies](https://mlsanity.com/paper/2505.11470/) (2025)
- [FoodTaxo: Generating Food Taxonomies with Large Language Models](https://mlsanity.com/paper/2505.19838/) (2025)

## Abstract (arXiv)

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, "taxonomy completion", by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate <hypernym, hyponym> pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on <query, hypernym, hyponym> triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.