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We propose a knowledge graph verification (KGV) method which consists of a knowledge graph embedding (KGE) training stage and a KG triplet classification model ...
This study aims to design an effective method for determining the correctness of triplets in a biomedical KG. We propose a knowledge graph verification (KGV) ...
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges.
Missing: Verification. | Show results with:Verification.
We introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) ...
Missing: Architecture Verification.
A comprehensive, large-scale biomedical knowledge graph for AI-powered, data-driven biomedical research.
5 days ago · This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. Existing state-of-the-art( ...
Missing: Biomedical Verification.
Duration: 1:04:10
Posted: Sep 25, 2023
Posted: Sep 25, 2023
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Jun 15, 2023 · HetioNet is a biomedKG of 11 types of nodes and 24 edge types, from 29 publicly available data sources. HetioNet is designed to integrate every ...
Oct 1, 2023 · In this paper, we combine the techniques of expert systems, graph neural networks, and knowledge graphs to propose a disease guidance model
To address this issue, this study introduces a novel model, KGCNN, designed to com- prehensively tackle DDI prediction tasks by considering spatial.