# Improved Knowledge Graph Embedding using Background Taxonomic Information

## TL;DR

This paper introduces a simple yet effective method to incorporate background taxonomic information into knowledge graph embeddings, improving their expressiveness and prediction accuracy.

## Contribution

It presents minimal modifications to existing models to enable the injection of taxonomic information and proves the model's full expressiveness under certain conditions.

## Key findings

- Enhanced embedding models respect subclass and subproperty constraints.
- Experimental results show improved performance on public knowledge graphs.
- Model remains simple yet effective in leveraging taxonomic background information.

## Abstract

Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. We show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. We show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. Moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. Experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective.