Aligning Embedding Spaces and Applications for Knowledge Graphs

Abstract

In previous chapters we have seen a variety of ways to train models to derive embedding spaces for words and concepts and other nodes in knowledge graphs. As you often do not have control over the full training procedure, you may find yourself with several embedding spaces which have (conceptually) overlapping vocabularies. How can you best combine such embedding spaces?. In this chapter we look at various techniques for aligning disparate embedding spaces. This is particularly useful in hybrid settings like when using embedding spaces for knowledge graph curation and interlinking.