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http://dx.doi.org/10.7838/jsebs.2021.26.3.067

Improving Embedding Model for Triple Knowledge Graph Using Neighborliness Vector  

Cho, Sae-rom (School of Electrical and Computer Engineering, University of Seoul)
Kim, Han-joon (School of Electrical and Computer Engineering, University of Seoul)
Publication Information
The Journal of Society for e-Business Studies / v.26, no.3, 2021 , pp. 67-80 More about this Journal
Abstract
The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.
Keywords
Knowledge Graph; Node Embedding; Triple Graph; Convolutional Network; Graph Feature Extraction; Machine Learning;
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