A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Complex Embedding with Type Constraints for Link Prediction
2022
Entropy
Large-scale knowledge graphs not only store entities and relations but also provide ontology-based information about them. Type constraints that exist in this information are of great importance for link prediction. In this paper, we proposed a novel complex embedding method, CHolE, in which complex circular correlation was introduced to extend the classic real-valued compositional representation HolE to complex domains, and type constraints were integrated into complex representational
doi:10.3390/e24030330
pmid:35327841
pmcid:PMC8947114
fatcat:c5tkpl5ycjfw5fepxgobii3kgu