Results 31 to 40 of about 177,158 (279)
Relation path embedding in knowledge graphs [PDF]
Neural Computing and Applications, 2018 Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths.
Xixun Lin +4 more
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Knowledge Graph Embedding via Graph Attenuated Attention Networks
IEEE Access, 2020 Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural
Rui Wang +4 more
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Triple Context-Based Knowledge Graph Embedding
IEEE Access, 2018 Knowledge graph embedding aims to represent entities and relations of a knowledge graph in continuous vector spaces. It has increasingly drawn attention for its ability to encode semantics in low dimensional vectors as well as its outstanding performance
Huan Gao, Jun Shi, Guilin Qi, Meng Wang
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Binarized Knowledge Graph Embeddings [PDF]
, 2019 Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC(CP) tensor decomposition, KGC on existing knowledge graphs is impractical in resource-limited environments, as a ...
Kishimoto, Koki +4 more
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Knowledge Graph Embedding Based Collaborative Filtering
IEEE Access, 2020 Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications.
Yuhang Zhang, Jun Wang, Jie Luo
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On Training Knowledge Graph Embedding Models
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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Graph Few-shot Learning via Knowledge Transfer
, 2020 Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating ...
Chawla, Nitesh V. +7 more
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Interaction Embeddings for Prediction and Explanation in Knowledge Graphs [PDF]
, 2019 Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related ...
Bordes Antoine +14 more
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Embedding models for episodic knowledge graphs [PDF]
Journal of Web Semantics, 2019 26 ...
Ma, Yunpu +2 more
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SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph
IEEE Access, 2021 Graph embedding models are widely used in knowledge graph completion (KGC) task. However, most models are based on the assumption that knowledge is completely certain, and this is inconsistent with real-world situations.
Jingbin Wang +3 more
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