# Embedding Method for Knowledge Graph with Densely Defined Ontology

arXiv: https://arxiv.org/abs/2504.02889 · HTML: https://mlsanity.com/paper/2504.02889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [A survey of embedding models of entities and relationships for knowledge graph completion](https://mlsanity.com/paper/1703.08098/) (2017)
- [Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System](https://mlsanity.com/paper/1908.08184/) (2019)

## Abstract (arXiv)

Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologies, specifically the relationships between properties. This study proposes a KGE model, TransU, designed for knowledge graphs with well-defined ontologies that incorporate relationships between properties. The model treats properties as a subset of entities, enabling a unified representation. We present experimental results using a standard dataset and a practical dataset.