Results 41 to 50 of about 175,691 (261)
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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Efficient Parallel Translating Embedding For Knowledge Graphs
, 2018 Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the ...
Abadi Martín +7 more
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Learning graph attention-aware knowledge graph embedding
Neurocomputing, 2021 The knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning knowledge graph embeddings straightforwardly and intuitively without subtly considering the ...
Li, C. +6 more
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Knowledge graph embedding based on semantic hierarchy
Cognitive Robotics, 2022 In view of the current knowledge graph embedding, it mainly focuses on symmetry/opposition, inversion and combination of relationship patterns, and does not fully consider the structure of the knowledge graph. We propose a Knowledge Graph Embedding Based
Fan Linjuan +3 more
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KBGAN: Adversarial Learning for Knowledge Graph Embeddings
, 2018 We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is ...
Cai, Liwei, Wang, William Yang
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Learning Triple Embeddings from Knowledge Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem
Fionda, Valeria, Pirrò, Giuseppe
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Quaternion Knowledge Graph Embeddings
, 2019 Accepted by NeurIPS ...
Zhang, Shuai +3 more
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Biomedical Knowledge Graph Embeddings with Negative Statements
, 2023 A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under an Open World Assumption.
Rita T. Sousa +3 more
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Knowledge Graph Embedding with Iterative Guidance from Soft Rules
, 2017 Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules,
Guo, Li +4 more
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Fast Variational Knowledge Graph Embedding
2024 IEEE International Conference on Quantum Computing and Engineering (QCE)Embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation.
Giri, Pulak Ranjan +2 more
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