Knowledge Graph Embedding Model with the Nearest Neighbors Based on Improved KNN
In order to better represent the rare entities with a small number of neighbors,this pa-per 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 Neigh-bor algorithm and encoded by the graph memory network.Through the analysis of the experi-mental 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 prob-lem and improving the knowledge representation performance.
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