Results 31 to 40 of about 175,691 (261)
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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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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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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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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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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A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
Applied Sciences, 2019 Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge ...
Yanjun Wang +7 more
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Embedding Uncertain Knowledge Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of ...
Chen, Xuelu +4 more
openaire +3 more sources