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.
@article{arxiv.2002.09247,
title = {Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods},
author = {Russa Biswas and Mehwish Alam and Harald Sack},
journal= {arXiv preprint arXiv:2002.09247},
year = {2020}
}