# Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods

arXiv: https://arxiv.org/abs/2002.09247 · HTML: https://mlsanity.com/paper/2002.09247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

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

- [On the invertibility of a voice privacy system using embedding alignement](https://mlsanity.com/paper/2110.05431/) (2021)
- [Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence](https://mlsanity.com/paper/2411.19113/) (2024)

## Abstract (arXiv)

Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG embeddings for knowledge-driven applications such as question answering, named entity disambiguation, knowledge graph completion, etc., alignment of different KG embedding spaces is necessary. In addition to multilinguality and domain-specific information, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This paper provides a theoretical analysis and comparison of the state-of-the-art alignment methods between two embedding spaces representing entity-entity and entity-word. This paper also aims at assessing the capability and short-comings of the existing alignment methods on the pretext of different applications.