Methods › Natural Language Processing › Taxonomy Expansion Models › TaxoExpan
TaxoExpan
Introduced by Jiaming Shen et al. in TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
TaxoExpan is a self-supervised taxonomy expansion framework. It automatically generates a set of pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. TaxoExpan features: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network 26 Jan 2020 · 3 repositories · arXiv:2001.09522Syntology ran 1 of 2 samples · 1 unverified
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Graph Neural Network | 1 |
| Position | 1 |
| Product Recommendation | 1 |
| Taxonomy Expansion | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections