ML Anthology

Learning Multi-Faceted Knowledge Graph Embeddings for Natural Language Processing

Muhao Chen, Carlo Zaniolo
IJCAI 2017 pp. 5169-5170
doi:10.24963/IJCAI.2017/744 /ijcai/2017/chen2017ijcai-learning-a/

Abstract

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.

PDF IJCAI Semantic Scholar

Cite

Text

Chen and Zaniolo. "Learning Multi-Faceted Knowledge Graph Embeddings for Natural Language Processing." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/744

Markdown

[Chen and Zaniolo. "Learning Multi-Faceted Knowledge Graph Embeddings for Natural Language Processing." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/chen2017ijcai-learning-a/) doi:10.24963/IJCAI.2017/744

BibTeX

@inproceedings{chen2017ijcai-learning-a,
  title     = {{Learning Multi-Faceted Knowledge Graph Embeddings for Natural Language Processing}},
  author    = {Chen, Muhao and Zaniolo, Carlo},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {5169-5170},
  doi       = {10.24963/IJCAI.2017/744},
  url       = {https://mlanthology.org/ijcai/2017/chen2017ijcai-learning-a/}
}