Machine Learning-Augmented Ontology-Based Data Access for Renewable Energy Data
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929547025317888 |
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| author | Calautti, Marco Duranti, Damiano Giorgini, Paolo |
| author_facet | Calautti, Marco Duranti, Damiano Giorgini, Paolo |
| contents | Managing the growing data from renewable energy production plants for effective decision-making often involves leveraging Ontology-based Data Access (OBDA), a well-established approach that facilitates querying diverse data through a shared vocabulary, presented in the form of an ontology. Our work addresses one of the common problems in this context, deriving from feeding complex class hierarchies defined by such ontologies from fragmented and imbalanced (w.r.t. class labels) data sources. We introduce an innovative framework that enhances existing OBDA systems. This framework incorporates a dynamic class management approach to address hierarchical classification, leveraging machine learning. The primary objectives are to enhance system performance, extract richer insights from underrepresented data, and automate data classification beyond the typical capabilities of basic deductive reasoning at the ontological level. We experimentally validate our methodology via real-world, industrial case studies from the renewable energy sector, demonstrating the practical applicability and effectiveness of the proposed solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12734 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Augmented Ontology-Based Data Access for Renewable Energy Data Calautti, Marco Duranti, Damiano Giorgini, Paolo Databases Managing the growing data from renewable energy production plants for effective decision-making often involves leveraging Ontology-based Data Access (OBDA), a well-established approach that facilitates querying diverse data through a shared vocabulary, presented in the form of an ontology. Our work addresses one of the common problems in this context, deriving from feeding complex class hierarchies defined by such ontologies from fragmented and imbalanced (w.r.t. class labels) data sources. We introduce an innovative framework that enhances existing OBDA systems. This framework incorporates a dynamic class management approach to address hierarchical classification, leveraging machine learning. The primary objectives are to enhance system performance, extract richer insights from underrepresented data, and automate data classification beyond the typical capabilities of basic deductive reasoning at the ontological level. We experimentally validate our methodology via real-world, industrial case studies from the renewable energy sector, demonstrating the practical applicability and effectiveness of the proposed solution. |
| title | Machine Learning-Augmented Ontology-Based Data Access for Renewable Energy Data |
| topic | Databases |
| url | https://arxiv.org/abs/2410.12734 |