Veröffentlichung: 2020 Februar
Art der Veröffentlichung: arxiv article
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 informa- tion, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This paper pro- vides a theoretical analysis and comparison of the state-of-the-art align- ment 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 pretext of different applications.
DOI Link: abs/2002.09247 CoRR abs/2002.09247