A Performance Optimization Scheme for Migrating Hive Data to Neo4j Database

Liú, Dan; Li, Ming Ming

doi:10.1109/is3c.2018.00010

Cited by 5 publications

(1 citation statement)

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“…Knowledge extraction technologies, such as data mining, natural language processing, and deep learning, are designed to extract entities and relations from unstructured industrial resources [31][32][33]. Researchers also enhance industrial knowledge graphs with generic knowledge graphs and exploit the storage of industrial knowledge graphs [34][35][36][37]. However, at present, industrial knowledge graphs are still entangled with the emerging resources, the knowledge mining of possible implicit relations between resources, and the collaboration of domain experts within the graph construction [38].…”

Section: Industrial Knowledge Graphmentioning

confidence: 99%

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

Liu

et al. 2022

Int J Comput Intell Syst

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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.

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