SeSICL: Semantic and Structural Integrated Contrastive Learning for Knowledge Graph Error Detection-Reference-Cited by-同舟云学术

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SeSICL: Semantic and Structural Integrated Contrastive Learning for Knowledge Graph Error Detection

Published:2024 Issue: Volume:12 Page:56088-56096
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Liu Xingyu¹^ORCID,Tang Jielong²,Li Mengyang³,Han Junmei⁴,Xiao Gang⁴,Jiang Jianchun³^ORCID

Affiliation:

1. State Key Laboratory of Intelligent Game, Institute of Software, Chinese Academy of Sciences, Beijing, China

2. School of Artificial Intelligence, Sun Yat-sen University, Guangzhou, China

3. Integrative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, China

4. Systemic Research Department, National Key Laboratory for Complex Systems Simulation, Systems Engineering Institute, Beijing, AMS, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10489925.pdf?arnumber=10489925

Reference55 articles.

1. Automatically acquiring a semantic network of related concepts

2. PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

3. Knowledge Graph Convolutional Networks for Recommender Systems

4. A review: Knowledge reasoning over knowledge graph

5. Knowledge Graph Embedding With Iterative Guidance From Soft Rules

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