Abstract
Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.- Anthology ID:
- 2020.textgraphs-1.1
- Volume:
- Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–14
- Language:
- URL:
- https://aclanthology.org/2020.textgraphs-1.1
- DOI:
- 10.18653/v1/2020.textgraphs-1.1
- Cite (ACL):
- Dat Quoc Nguyen. 2020. A survey of embedding models of entities and relationships for knowledge graph completion. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 1–14, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- A survey of embedding models of entities and relationships for knowledge graph completion (Nguyen, TextGraphs 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.textgraphs-1.1.pdf
- Code
- additional community code
- Data
- FB15k, FB15k-237, NELL, WN18, WN18RR