Semantic- and relation-based graph neural network for knowledge graph completion
Author:
Funder
National Natural Science Foundation of China
Natural Science Research Project of Anhui Educational Committee
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05482-2.pdf
Reference44 articles.
1. Yu M, Jiang T, Yu J, Zhao M, Guo J, Yang M, Yu R, Li X (2023) Sepake: a structure-enhanced and position-aware knowledge embedding framework for knowledge graph completion. Appl Intell 53(20):23113–23123
2. Ta HT, Rahman ABS, Majumder N, Hussain A, Najjar L, Howard N, Poria S, Gelbukh A (2023) Wikides: A wikipedia-based dataset for generating short descriptions from paragraphs. Inf Fusion 90:265–282
3. Formica A, Taglino F (2023) Semantic relatedness in dbpedia: A comparative and experimental assessment. Inf Sci 621:474–505
4. Zeb A, Saif S, Chen J, Yu JJ, Jiang Q, Zhang D (2024) Cope: Composition-based poincaré embeddings for link prediction in knowledge graphs. Inf Sci 662:120197
5. Wang J, Li W, Liu W, Wang C, Jin Q (2023) Enabling inductive knowledge graph completion via structure-aware attention network. Appl Intell 53(21):25003–25027
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