Learning Entity Type Embeddings for Knowledge Graph Completion

By Changsung Moon, Paul Jones, Nagiza F. Samatova. 2017

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Avishek Joey Bose
2. William L. Hamilton
3. Sang-Min Park
4. Young-Gab Kim
5. Jure Leskovec
6. Rex Ying
7. Marinka Zitnik
8. Suhang Wang

In Management Science and Operations Research

1. Avishek Joey Bose
2. William L. Hamilton

Citations in the corpus

• Compositional Fairness Constraints for Graph Embeddings (2019, methodology): “Taking the entity attribute labels from Moon et al. (2017), we used the 3 - most common attribute labels (e.g., /award/award nominee) as “sensitive” attributes.”
• A Metaverse: Taxonomy, Components, Applications, and Open Challenges (2022, background): “Methods for connecting entities by structural learning include link prediction, nonlinear relationships, joint inference, and relationship classification methods [99]–[108].”