# Ontology Matching Through Absolute Orientation of Embedding Spaces

arXiv: https://arxiv.org/abs/2204.04040 · HTML: https://mlsanity.com/paper/2204.04040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.07659/) (2020)
- [Putting RDF2vec in Order](https://mlsanity.com/paper/2108.05280/) (2021)
- [Walk this Way! Entity Walks and Property Walks for RDF2vec](https://mlsanity.com/paper/2204.02777/) (2022)

## Built on by (most cited first, 2 of 2)

- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence](https://mlsanity.com/paper/2411.19113/) (2024)

## Abstract (arXiv)

Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces. Next to the approach, the paper presents a first, preliminary evaluation using synthetic and real-world datasets. We find in experiments with synthetic data, that the approach works very well on similarly structured graphs; it handles alignment noise better than size and structural differences in the ontologies.