Yajing Ma1,2,3, Gulila Altenbek1,2,3,*, Yingxia Yu1
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486
Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event representation and establish effective correlations,… More >
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