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

TL;DR

This paper introduces TaxoOrder, a self-supervised framework that improves taxonomy expansion by discovering local hypernym-hyponym structures and determining the optimal insertion order, thereby enhancing the quality of expanded taxonomies.

Contribution

The paper presents a novel self-supervised method that models dependencies among new concepts and optimizes their insertion order in taxonomy expansion tasks.

Findings

TaxoOrder improves taxonomy quality across various metrics.

It effectively discovers local hypernym-hyponym structures.

Enhances existing taxonomy expansion systems with minimal integration effort.

Abstract

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.