SEPAKE: a structure-enhanced and position-aware knowledge embedding framework for knowledge graph completion
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04723-0.pdf
Reference46 articles.
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3. Li W, Peng R, Li Z (2022) Improving knowledge graph completion via increasing embedding interactions. Appl Intell 52(8):9289–9307
4. Guo J, Fan Y, Pang L, Yang L, Ai Q, Zamani H, Wu C, Croft WB, Cheng X (2020) A deep look into neural ranking models for information retrieval. Inf Process Manag 57(6):102067
5. Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637
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1. Relation representation based on private and shared features for adaptive few-shot link prediction;Journal of Intelligent Information Systems;2024-04-10
2. Semantic- and relation-based graph neural network for knowledge graph completion;Applied Intelligence;2024-04
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