  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.
