A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs
It provides a comprehensive overview for researchers working on embedding alignment, but is incremental as it synthesizes existing work rather than introducing new methods.
This paper surveys existing methods for aligning embedding spaces across different data sources like language and knowledge graphs, classifying techniques and discussing benchmarks to motivate further research in this area.
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.