Automated Knowledge Graph Learning in Industrial Processes
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910510091337728 |
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| author | Ammann, Lolitta Martinez-Gil, Jorge Mayr, Michael Chasparis, Georgios C. |
| author_facet | Ammann, Lolitta Martinez-Gil, Jorge Mayr, Michael Chasparis, Georgios C. |
| contents | Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Knowledge Graph Learning in Industrial Processes Ammann, Lolitta Martinez-Gil, Jorge Mayr, Michael Chasparis, Georgios C. Machine Learning Artificial Intelligence Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters. |
| title | Automated Knowledge Graph Learning in Industrial Processes |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.02106 |