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| 主要な著者: | , , |
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| フォーマット: | Recurso digital |
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Zenodo
2025
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| オンライン･アクセス: | https://doi.org/10.5281/zenodo.17650541 |
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- <p>The wdn-knowledge-graph is a Python package developed as part of the DiTEC project that enables the conversion of water distribution network (WDN) data from EPANET .inp files into RDF-based knowledge graphs in Turtle (.ttl) format using a domain-specific WDN ontology. The package provides a command-line interface and Python API for automated knowledge graph construction, capturing the physical components and relationships within water distribution networks, including pipes, junctions, reservoirs, tanks, pumps, and valves. Built on established Python libraries including WNTR for water network modeling, RDFLib for RDF data processing, and NetworkX for network analysis, the package facilitates semantic data representation and enables the conversion of generated knowledge graphs to NetworkX format for subsequent analysis and manipulation. This tool supports the application of semantic technologies and machine learning methods to critical water infrastructure systems, enabling advanced data integration, querying capabilities through SPARQL, and semantic rule learning from Internet of Things data in the water distribution domain.</p> <p>CITATION: If you use this software in your work, please cite our research:</p> <p><span>Erkan Karabulut, Paul Groth, and Victoria Degeler. "Learning Semantic Association Rules from Internet of Things Data". Neurosymbolic Artificial Intelligence, 2025:1. doi:10.1177/29498732251377518.</span> <a href="https://journals.sagepub.com/doi/10.1177/29498732251377518" target="_blank" rel="noopener"> </a></p>