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Data Catalog Vocabulary (DCAT) |
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======================================================================================================================== |
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Overview |
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-------- |
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Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability |
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between data catalogs published on the Web. This document defines the schema and provides examples for its use. |
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DCAT enables a publisher to describe datasets and data services in a catalog using a standard model |
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and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. |
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This can increase the discoverability of datasets and data services. It also makes it possible |
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to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs |
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in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata |
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can serve as a manifest file as part of the digital preservation process. |
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:Domain: Scholarly Knowledge |
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:Category: Data Catalogs |
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:Current Version: 3.0 |
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:Last Updated: 22 August 2024 |
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:Creator: Digital Enterprise Research Institute (DERI) |
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:License: W3C Document License |
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:Format: RDF |
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:Download: `Data Catalog Vocabulary (DCAT) Homepage <https://www.w3.org/TR/vocab-dcat-3/>`_ |
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Graph Metrics |
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------------- |
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- **Total Nodes**: 987 |
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- **Total Edges**: 1313 |
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- **Root Nodes**: 7 |
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- **Leaf Nodes**: 908 |
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Knowledge coverage |
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------------------ |
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- Classes: 10 |
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- Individuals: 0 |
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- Properties: 39 |
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Hierarchical metrics |
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-------------------- |
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- **Maximum Depth**: 3 |
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- **Minimum Depth**: 0 |
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- **Average Depth**: 2.42 |
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- **Depth Variance**: 0.58 |
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Breadth metrics |
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------------------ |
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- **Maximum Breadth**: 121 |
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- **Minimum Breadth**: 7 |
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- **Average Breadth**: 54.50 |
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- **Breadth Variance**: 2135.25 |
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Dataset Statistics |
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------------------ |
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Generated Benchmarks: |
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- **Term Types**: 0 |
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- **Taxonomic Relations**: 8 |
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- **Non-taxonomic Relations**: 0 |
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- **Average Terms per Type**: 0.00 |
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Usage Example |
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.. code-block:: python |
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from ontolearner.ontology import DCAT |
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# Initialize and load ontology |
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ontology = DCAT() |
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ontology.load("path/to/ontology.RDF") |
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# Extract datasets |
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data = ontology.extract() |
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# Access specific relations |
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term_types = data.term_typings |
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taxonomic_relations = data.type_taxonomies |
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non_taxonomic_relations = data.type_non_taxonomic_relations |
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