# DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space

arXiv: https://arxiv.org/abs/2302.11165 · HTML: https://mlsanity.com/paper/2302.11165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 22 Feb 2023.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (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)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (2020)
- [STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths](https://mlsanity.com/paper/2006.10217/) (2020)
- [Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings](https://mlsanity.com/paper/1906.02002/) (2019)
- [Taxonomy Completion via Triplet Matching Network](https://mlsanity.com/paper/2101.01896/) (2021)
- [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)
- [Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2101.11268/) (2021)
- [A User-Centered Concept Mining System for Query and Document Understanding at Tencent](https://mlsanity.com/paper/1905.08487/) (2019)
- [Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion](https://mlsanity.com/paper/2104.03682/) (2021)

## Abstract (arXiv)

Taxonomy expansion is the process of incorporating a large number of additional nodes (i.e., "queries") into an existing taxonomy (i.e., "seed"), with the most important step being the selection of appropriate positions for each query. Enormous efforts have been made by exploring the seed's structure. However, existing approaches are deficient in their mining of structural information in two ways: poor modeling of the hierarchical semantics and failure to capture directionality of is-a relation. This paper seeks to address these issues by explicitly denoting each node as the combination of inherited feature (i.e., structural part) and incremental feature (i.e., supplementary part). Specifically, the inherited feature originates from "parent" nodes and is weighted by an inheritance factor. With this node representation, the hierarchy of semantics in taxonomies (i.e., the inheritance and accumulation of features from "parent" to "child") could be embodied. Additionally, based on this representation, the directionality of is-a relation could be easily translated into the irreversible inheritance of features. Inspired by the Darmois-Skitovich Theorem, we implement this irreversibility by a non-Gaussian constraint on the supplementary feature. A log-likelihood learning objective is further utilized to optimize the proposed model (dubbed DNG), whereby the required non-Gaussianity is also theoretically ensured. Extensive experimental results on two real-world datasets verify the superiority of DNG relative to several strong baselines.