A type-augmented knowledge graph embedding framework for knowledge graph completion [PDF]
Scientific Reports, 2023 Knowledge graphs (KGs) are of great importance to many artificial intelligence applications, but they usually suffer from the incomplete problem.
Peng He +4 more
doaj +3 more sources
HTINet2: herb–target prediction via knowledge graph embedding and residual-like graph neural network [PDF]
Briefings in BioinformaticsTarget identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets.
Pengbo Duan, Kuo Yang, Xiaoyan Xing
exaly +3 more sources
On Training Knowledge Graph Embedding Models [PDF]
Information, 2021 Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner.
Sameh K. Mohamed +2 more
doaj +3 more sources
Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining. [PDF]
BMC Bioinformatics, 2023 Background The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified.
Djeddi WE +3 more
europepmc +2 more sources
Review of Research Progress on Knowledge Graph Embedding [PDF]
Jisuanji gongchengWith the continuous development of big data and artificial intelligence technologies, knowledge graph embedding is developing rapidly, and knowledge graph applications are becoming increasingly widespread.
MA Hengzhi, QIAN Yurong, LENG Hongyong, WU Haipeng, TAO Wenbin, ZHANG Yiyang
doaj +2 more sources
Knowledge Graph Embedding by Dynamic Translation
IEEE Access, 2017 Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the ...
Liang Chang +5 more
doaj +2 more sources
Ultrahyperbolic Knowledge Graph Embeddings
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of ...
Staab, S +13 more
core +3 more sources
Knowledge Graph Embedding Model with Entity Description on Cement Manufacturing Domain [PDF]
Jisuanji kexueTo address the problem that many knowledge graph embedding models lack the consideration of semantic information when performing knowledge embedding and cannot extract the semantic information of entities specialized in cement manufactu-ring domain well ...
ZHOU Honglin, SONG Huazhu, ZHANG Juan
doaj +2 more sources
An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding
Applied Sciences, 2020 Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks.
Su Jeong Choi +2 more
doaj +2 more sources
FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning
International Journal of Computational Intelligence SystemsAs knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information.
Wei Huang +5 more
doaj +2 more sources