A survey of embedding models of entities and relationships for knowledge graph completion

Dat Quoc Nguyen


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)
Editors:
Dmitry Ustalov, Swapna Somasundaran, Alexander Panchenko, Fragkiskos D. Malliaros, Ioana Hulpuș, Peter Jansen, Abhik Jana
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.textgraphs-1.1.pdf
Optional supplementary material:
 2020.textgraphs-1.1.OptionalSupplementaryMaterial.pdf
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