Results 11 to 20 of about 7,645,086 (295)
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
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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
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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
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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
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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
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Knowledge Graph Embedding: An Overview
APSIPA Transactions on Signal and Information ProcessingMany mathematical models have been leveraged to design em-beddings for representing Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks. These mathematically-inspired models are not only highly scalable for inference in large KGs, but also have many explainable advantages in modeling different relation patterns ...
Xiou Ge +3 more
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Mathematics, 2022 Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph.
Pengfei Zhang +4 more
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Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
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Survey on Applications of Knowledge Graph Embedding in Recommendation Tasks [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation systems are designed to recommend personalized content to improve user experience. At present, the recommendation systems still face some challenges such as poor interpretability, cold start problem and serialized recommendation modeling ...
TIAN Xuan, CHEN Hangxue
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Spectral Geometry for Structural Pattern Recognition [PDF]
, 2010 Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis
El Ghawalby, Hewayda
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