@inproceedings{nguyen-2020-survey,
title = "A survey of embedding models of entities and relationships for knowledge graph completion",
author = "Nguyen, Dat Quoc",
booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.textgraphs-1.1",
doi = "10.18653/v1/2020.textgraphs-1.1",
pages = "1--14",
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.",
}
Markdown (Informal)
[A survey of embedding models of entities and relationships for knowledge graph completion](https://aclanthology.org/2020.textgraphs-1.1) (Nguyen, TextGraphs 2020)
ACL