  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.
