چکیده مقاله
Knowledge graphs KGs play a vital role in enhancing search results and recommendationsystems With the rapid increase in the size of the KGs, they are becoming inaccuracy andincomplete This problem can be solved by the knowledge graph completion methods In this paperwe use a novel method for knowledge graph link prediction named Node2vec Enhanced GraphConvolutional Network NE GCN , for computing pairwise occurrences of entity relation pairs inthe dataset to construct a joint learning model Given a knowledge graph, NE GCN constructs asingle graph considering entities and relations as individual nodes NE GCN then computesweights for edges among nodes based on the pairwise occurrence of entities and relations Next,uses Graph Convolution neural Network GCN to update vector representations for entity andrelation nodes This work opens up new possibilities for graph based learning models andrepresents a major leap in the field
کلیدواژهها
نویسندگان
Mohammadreza Ghaffarian
School of Engineering Science, University of Tehran, Tehran, Iran;
Rooholah Abedian
School of Engineering Science, University of Tehran, Tehran, Iran
Ali Moeini
School of Engineering Science, University of Tehran, Tehran, Iran
شیوه ارجاع
Ghaffarian, Mohammadreza and Abedian, Rooholah and Moeini, Ali,1402,NE-GCN: Advancing Knowledge Graph Link Prediction with Node2vec-Enhanced Graph Convolutional Networks,5th International Conference on Software Computing,Rudsar
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