A Vector Similarity-based Knowledge Graph Embedding Method for Predicting Missing Hierarchical Relationships in SNOMED CT-Reference-Cited by-同舟云学术

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A Vector Similarity-based Knowledge Graph Embedding Method for Predicting Missing Hierarchical Relationships in SNOMED CT

Published:2024-01-19 Issue: Volume:2017 Page:16-20
ISSN:
Container-title:2024 4th International Conference on Neural Networks, Information and Communication (NNICE)
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Author:

Huang Xiaole¹,Zheng Fengbo¹,Jiang Lifen¹,Liang Yan¹,Li Ying¹

Affiliation:

1. Tianjin Normal University,School of Computer and Information Engineering,Tianjin,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10498130/10498125/10498282.pdf?arnumber=10498282

Reference12 articles.

1. Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities;Quesada-Martínez;in MIE,2016

2. Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT

3. Quality Assurance of NCI Thesaurus by Mining Structural-Lexical Patterns;Abeysinghe

4. Leveraging horizontal density differences between ontologies to identify missing child concepts: a proof of concept;Keloth

5. A lexical-based approach for exhaustive detection of missing hierarchical IS-A relations in SNOMED CT;Zheng

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