Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

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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Bibliographic Details
Main Authors: Yu Zhao, Huali Feng, Patrick Gallinari
Format: Article
Language:English
Published: MDPI AG 2019-11-01
Series:Entropy
Subjects:
knowledge graph
embedding learning
cross entropy
noise detection
triple trustiness
Online Access:https://www.mdpi.com/1099-4300/21/11/1083

Internet

https://www.mdpi.com/1099-4300/21/11/1083

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