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knowledge graph embedding
Machine learning embedding of knowledge graph
In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their meaning. Leveraging their embedded representation, knowledge graphs can be used for various applications such as link prediction, triple classification, entity recognition, clustering, and relation extraction.
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Texto en inglés Aún no hay artículo en tu idioma: extracto en inglés.
In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their meaning. Leveraging their embedded representation, knowledge graphs can be used for various applications such as link prediction, triple classification, entity recognition, clustering, and relation extraction.
Texto: Wikipedia en inglés, CC BY-SA 4.0. · Imagen: EdoardoRamalli (CC BY-SA 4.0) ·
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