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Reference.org
Knowledge graph embedding
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<p>In <a href="/facts/Representation_learning/6GYV0mux">representation learning</a>, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> task of learning a low-dimensional representation of a <a href="/facts/Knowledge_graph/IgQEyHPp">knowledge graph</a>'s entities and relations while preserving their <a href="/facts/Semantics/1L4WwLAP">semantic</a> meaning. Leveraging their <a href="/facts/Embedding_(machine_learning)/sj8rzADC">embedded</a> representation, knowledge graphs (KGs) can be used for various applications such as <a href="/facts/Link_prediction/vd9FbAxe">link prediction</a>, triple classification, entity recognition, <a href="/facts/Cluster_analysis/HUeDnWPt">clustering</a>, and <a href="/facts/Relation_extraction/mqhXTxLN">relation extraction</a>. </p>