Article: Construction of digital art knowledge graph based on deep recurrent neural network Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.11 pp.18 - 37 Abstract: This study presents a method for constructing digital art knowledge graphs based on deep recurrent neural network (DRNN). A digital art knowledge graph is initially constructed by extracting visual features with ResNet50 and identifying textual entities via a CNN-BiLSTM-CRF model. Then, a DRDA model with bidirectional gated recurrent unit (GRU) and neighbour-aware attention is proposed for graph completion. Experiments on DBPedia50k and DBPedia500k show DRDA's superiority over three baselines. On DBPedia50k, DRDA improves head prediction MRR by up to 55% and achieves the lowest MR in tail prediction, though trailing slightly in Hits@10. On DBPedia500k, DRDA consistently outperforms baselines with MR reductions of 59-406 and MRR gains of 2%-19%. Further analysis identifies optimal depth and neighbour parameters, validating the model's scalability and its effectiveness in capturing complex semantic dependencies in large-scale multimodal art data. Inderscience Publishers - linking academia, business and industry through research

Title: Construction of digital art knowledge graph based on deep recurrent neural network

Authors: Huan Wang

Addresses: College of Art and Design, Shanghai Normal University Tianhua College, Shanghai, 201815, China

Abstract: This study presents a method for constructing digital art knowledge graphs based on deep recurrent neural network (DRNN). A digital art knowledge graph is initially constructed by extracting visual features with ResNet50 and identifying textual entities via a CNN-BiLSTM-CRF model. Then, a DRDA model with bidirectional gated recurrent unit (GRU) and neighbour-aware attention is proposed for graph completion. Experiments on DBPedia50k and DBPedia500k show DRDA's superiority over three baselines. On DBPedia50k, DRDA improves head prediction MRR by up to 55% and achieves the lowest MR in tail prediction, though trailing slightly in Hits@10. On DBPedia500k, DRDA consistently outperforms baselines with MR reductions of 59-406 and MRR gains of 2%-19%. Further analysis identifies optimal depth and neighbour parameters, validating the model's scalability and its effectiveness in capturing complex semantic dependencies in large-scale multimodal art data.

Keywords: digital art; knowledge graph; deep recurrent neural network; DRNN.

DOI: 10.1504/IJICT.2026.151713

International Journal of Information and Communication Technology, 2026 Vol.27 No.11, pp.18 - 37

Received: 02 Sep 2025
Accepted: 22 Nov 2025
Published online: 16 Feb 2026 *

Title: Construction of digital art knowledge graph based on deep recurrent neural network

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