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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Главные авторы:	Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati, Francesco Osborne, Andrey Kravchenko, Simone Angioni, Angelo Salatino, Diego Reforgiato Recupero, Enrico Motta, Jens Lehmann
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IEEE 2021-01-01
Серии:	IEEE Access
Предметы:	Scholarly data knowledge graphs knowledge graph embeddings loss functions link prediction scholarly communication
Online-ссылка:	https://ieeexplore.ieee.org/document/9514834/
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