# Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion

arXiv: https://arxiv.org/abs/2104.03682 · HTML: https://mlsanity.com/paper/2104.03682/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering](https://mlsanity.com/paper/1812.09551/) (2018)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [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)
- [Octet: Online Catalog Taxonomy Enrichment with Self-Supervision](https://mlsanity.com/paper/2006.10276/) (2020)
- [Taxonomy Completion via Triplet Matching Network](https://mlsanity.com/paper/2101.01896/) (2021)
- [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)

## Built on by (most cited first, 6 of 6)

- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations](https://mlsanity.com/paper/2202.04887/) (2022)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation](https://mlsanity.com/paper/2303.14480/) (2023)
- [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)

## Abstract (arXiv)

Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks. One critical challenge is that, as data and business scope grow in real applications, existing taxonomies need to be expanded to incorporate new concepts. Previous works on taxonomy expansion process the new concepts independently and simultaneously, ignoring the potential relationships among them and the appropriate order of inserting operations. However, in reality, the new concepts tend to be mutually correlated and form local hypernym-hyponym structures. In such a scenario, ignoring the dependencies of new concepts and the order of insertion may trigger error propagation. For example, existing taxonomy expansion systems may insert hyponyms to existing taxonomies before their hypernym, leading to sub-optimal expanded taxonomies. To complement existing taxonomy expansion systems, we propose TaxoOrder, a novel self-supervised framework that simultaneously discovers the local hypernym-hyponym structure among new concepts and decides the order of insertion. TaxoOrder can be directly plugged into any taxonomy expansion system and improve the quality of expanded taxonomies. Experiments on the real-world dataset validate the effectiveness of TaxoOrder to enhance taxonomy expansion systems, leading to better-resulting taxonomies with comparison to baselines under various evaluation metrics.