Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain

Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (e.g., missing affiliations, refere...

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Bibliographic Details
Main Authors: Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati, Francesco Osborne, Andrey Kravchenko, Simone Angioni, Angelo Salatino, Diego Reforgiato Recupero, Enrico Motta, Jens Lehmann
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Scholarly data
knowledge graphs
knowledge graph embeddings
loss functions
link prediction
scholarly communication
Online Access:https://ieeexplore.ieee.org/document/9514834/

Internet

https://ieeexplore.ieee.org/document/9514834/

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