Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG const...

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Detalles Bibliográficos
Autores principales: Zhao, Yu, Feng, Huali, Gallinari, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514427/
http://dx.doi.org/10.3390/e21111083

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514427/
http://dx.doi.org/10.3390/e21111083

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