  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.
