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.
刘婕, 孙更新, 宾晟. 基于改进KNN近邻实体的知识图谱嵌入模型[J]. 复杂系统与复杂性科学, 2024, 21(2): 30-37.
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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