# A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs

arXiv: https://arxiv.org/abs/2010.13688 · HTML: https://mlsanity.com/paper/2010.13688/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP](https://mlsanity.com/paper/2408.04303/) (2024)
- [Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping](https://mlsanity.com/paper/2104.00878/) (2021)
- [Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation](https://mlsanity.com/paper/2310.14806/) (2023)
- [Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation](https://mlsanity.com/paper/2404.16880/) (2024)

## Abstract (arXiv)

Neural embedding approaches have become a staple in the fields of computer vision, natural language processing, and more recently, graph analytics. Given the pervasive nature of these algorithms, the natural question becomes how to exploit the embedding spaces to map, or align, embeddings of different data sources. To this end, we survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research. By gathering these diverse approaches into a singular survey, we hope to further motivate research into alignment of embedding spaces of varied data types and sources.