# Order Embeddings from Merged Ontologies using Sketching

arXiv: https://arxiv.org/abs/2101.02158 · HTML: https://mlsanity.com/paper/2101.02158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Order-Embeddings of Images and Language](https://mlsanity.com/paper/1511.06361/) (2015)
- [Hierarchical Embeddings for Hypernymy Detection and Directionality](https://mlsanity.com/paper/1707.07273/) (2017)
- [Specialising Word Vectors for Lexical Entailment](https://mlsanity.com/paper/1710.06371/) (2017)
- [Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection](https://mlsanity.com/paper/1710.00880/) (2017)

## Abstract (arXiv)

We give a simple, low resource method to produce order embeddings from ontologies. Such embeddings map words to vectors so that order relations on the words, such as hypernymy/hyponymy, are represented in a direct way. Our method uses sketching techniques, in particular countsketch, for dimensionality reduction. We also study methods to merge ontologies, in particular those in medical domains, so that order relations are preserved. We give computational results for medical ontologies and for wordnet, showing that our merging techniques are effective and our embedding yields an accurate representation in both generic and specialised domains.