A structure distinguishable graph attention network for knowledge base completion

• Published in: Neural computing & applications Vol. 33; no. 23; pp. 16005 - 16017
• Main Authors: Zhou, Xue, Hui, Bei, Zhang, Lizong, Ji, Kexi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2021; Springer Nature B.V
• Subjects: Agglomeration; Artificial Intelligence; Artificial neural networks; Coders; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Graphical representations; Image Processing and Computer Vision; Knowledge bases (artificial intelligence); Knowledge representation; Neighborhoods; Neural networks; Original Article; Probability and Statistics in Computer Science; Knowledge base completion; Graph attention network; Smoothing problem; Neighborhood aggregation scheme

A knowledge graph is a collection of triples, often represented in the form of “subject,” “relation,” “object.” The task of knowledge graph completion (KGC) is to automatically predict missing links by reasoning over the information already present in the knowledge graph. Recent popularization of gr...