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Complex Systems and Complexity Science  2024, Vol. 21 Issue (2): 30-37    DOI: 10.13306/j.1672-3813.2024.02.004
Complex Network Current Issue | Archive | Adv Search |
Knowledge Graph Embedding Model with the Nearest Neighbors Based on Improved KNN
LIU Jie, SUN Gengxin, BIN Sheng
College of Computer Science & Technology, Qingdao University, Qingdao 266071, China
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Abstract  In order to better represent the rare entities with a small number of neighbors, this paper proposes a knowledge graph embedding model based on the nearest neighbors (NNKGE), which uses the K-Nearest Neighbor algorithm to obtain the nearest neighbors of the target entity as extended information. Based on this, the relational nearest neighbors-based knowledge graph embedding model (RNNKGE) is proposed. To generate an enhanced entity representation, the nearest neighbors of the target entity in relation are obtained by the improved K-Nearest Neighbor algorithm and encoded by the graph memory network. Through the analysis of the experimental results on the public datasets, the above two models outperform the benchmark model (CoNE) in the case of using only the nearest neighbor nodes, alleviating the data sparsity problem and improving the knowledge representation performance.
Key wordsknowledge graph      knowledge graph embedding      neighbor nodes      K-nearest neighbor algorithm      graph memory network     
Received: 09 September 2022      Published: 17 July 2024
ZTFLH:  TP181  
  O157.5  
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LIU Jie
SUN Gengxin
BIN Sheng
Cite this article:   
LIU Jie,SUN Gengxin,BIN Sheng. Knowledge Graph Embedding Model with the Nearest Neighbors Based on Improved KNN[J]. Complex Systems and Complexity Science, 2024, 21(2): 30-37.
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https://fzkx.qdu.edu.cn/EN/10.13306/j.1672-3813.2024.02.004     OR     https://fzkx.qdu.edu.cn/EN/Y2024/V21/I2/30