Learning Multi-faceted Knowledge Graph Embeddings for Natural Language Processing

Knowledge graphs have challenged the present embedding-based approaches for representing their multifacetedness. To address some of the issues, we have investigated some novel approaches that (i) captures multilingual transitions on different language-specific versions of knowledge, and (ii) encodes the commonly existing monolingual knowledge with important relational properties and hierarchies. In addition, we propose the use of our approaches in a wide spectrum of NLP tasks that have not been well explored by related works.