In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning,[1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.[1][2][3] Leveraging their embedded representation, knowledge graphs (KGs) can be used for various applications such as link prediction, triple classification, entity recognition, clustering, and relation extraction.[1][4]
- ^ a b c Ji, Shaoxiong; Pan, Shirui; Cambria, Erik; Marttinen, Pekka; Yu, Philip S. (2021). "A Survey on Knowledge Graphs: Representation, Acquisition, and Applications". IEEE Transactions on Neural Networks and Learning Systems. PP (2): 494–514. arXiv:2002.00388. doi:10.1109/TNNLS.2021.3070843. hdl:10072/416709. ISSN 2162-237X. PMID 33900922. S2CID 211010433.
- ^ Mohamed, Sameh K; Nováček, Vít; Nounu, Aayah (2019-08-01). Cowen, Lenore (ed.). "Discovering Protein Drug Targets Using Knowledge Graph Embeddings". Bioinformatics. 36 (2): 603–610. doi:10.1093/bioinformatics/btz600. hdl:10379/15375. ISSN 1367-4803. PMID 31368482.
- ^ Lin, Yankai; Han, Xu; Xie, Ruobing; Liu, Zhiyuan; Sun, Maosong (2018-12-28). "Knowledge Representation Learning: A Quantitative Review". arXiv:1812.10901 [cs.CL].
- ^ Abu-Salih, Bilal; Al-Tawil, Marwan; Aljarah, Ibrahim; Faris, Hossam; Wongthongtham, Pornpit; Chan, Kit Yan; Beheshti, Amin (2021-05-12). "Relational Learning Analysis of Social Politics using Knowledge Graph Embedding". Data Mining and Knowledge Discovery. 35 (4): 1497–1536. arXiv:2006.01626. doi:10.1007/s10618-021-00760-w. ISSN 1573-756X. S2CID 219179556.
© MMXXIII Rich X Search. We shall prevail. All rights reserved. Rich X Search