Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

• Main Authors: Gu, F. (Author), Gu, X. (Author), Guo, J. (Author), Liu, Y. (Author), Wu, Y. (Author), Zhang, J. (Author)
• Format: Article
• Language: English
• Published: Springer Science and Business Media B.V. 2022
• Subjects: Based reasonings; Few-shot relational learning; Graphic methods; Industrial knowledge graph; Knowledge graph; Knowledge graphs; Learning models; Learning systems; Long tail; Relational learning; Resource conditions; Resource recommendation; Schema-based reasoning
• Online Access: View Fulltext in Publisher

 Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions. © 2022, The Author(s).