2024 journal article
Manufacturing service capability prediction with Graph Neural Networks
JOURNAL OF MANUFACTURING SYSTEMS, 74, 291–301.
author keywords: Node classification; Link prediction; Graph neural network; Manufacturing service capability; Manufacturing Service Knowledge Graph
open access via arxiv.org (repository)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
12. Responsible Consumption and Production
(Web of Science)
Source: Web Of Science
Added: May 28, 2024
In the current landscape, the predominant methods for identifying manufacturing capabilities from manufacturers rely heavily on keyword matching and semantic matching. However, these methods often fall short by either overlooking valuable hidden information or misinterpreting critical data. Consequently, such approaches result in an incomplete identification of manufacturers' capabilities. This underscores the pressing need for data-driven solutions to enhance the accuracy and completeness of manufacturing capability identification. To address the need, this study proposes a Graph Neural Network-based method for manufacturing service capability identification over a knowledge graph. To enhance the identification performance, this work introduces a novel approach that involves aggregating information from the graph nodes' neighborhoods as well as oversampling the graph data, which can be effectively applied across a wide range of practical scenarios. Evaluations conducted on a Manufacturing Service Knowledge Graph and subsequent ablation studies demonstrate the efficacy and robustness of the proposed approach. This study not only contributes a innovative method for inferring manufacturing service capabilities but also significantly augments the quality of Manufacturing Service Knowledge Graphs.