Semantic-aware event link reasoning over industrial knowledge graph embedding time series data

The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an industrial knowledge graph embedding time series data. Its knowledge graph skeleton is constructed through a specific manufacturing process. NLTK is used to transform technical documents into a structured industrial knowledge graph. We employ deep learning (DL)-based models to obtain semantic information related to product quality prediction using time series data collected from IoT devices. Then the prediction information is attached to the specified node in the knowledge graph. Thus, the knowledge graph will describe the dynamic semantic information of manufacturing contexts. Meanwhile, a dynamic event link reasoning model that uses graph embedding to aggregate manufacturing processes information is proposed. The implicit information with industrial temporal knowledge can be further mined and inferred. The case study has shown that the proposed knowledge graph link reasoning reflects dynamic temporal characteristics. Compared to the classical knowledge graph prediction models, our model is superior to the baseline methods.

Automated summary

This paper proposes a method of analyzing time series data based on an industrial knowledge graph, which effectively improves the knowledge processing efficiency of the manufacturing system by integrating temporal features and semantic information. By using deep learning models and graph embedding techniques, semantic information related to product quality prediction can be extracted from time series data and linked to the knowledge graph, describing the dynamic semantic information of the manufacturing environment.