Dublin Core Vocabulary (DublinCore)

The Dublin Core Schema is a small yet powerful vocabulary providing essential metadata elements for describing resources across diverse domains. Dublin Core Metadata can be used for multiple purposes including simple resource description, cross-standard metadata interoperability, and Linked Data cloud integration. It comprises fifteen core metadata elements (title, creator, subject, description, publisher, contributor, date, type, format, identifier, source, language, relation, coverage, rights) that are universally applicable across resource types. Dublin Core supports both simple and qualified metadata representation, enabling both basic and complex semantic annotation requirements. The vocabulary is language-independent and has become the de facto standard for resource description in digital libraries, institutional repositories, and data catalogs worldwide. Dublin Core facilitates semantic interoperability across heterogeneous information systems and enables automated resource discovery and management.

Example Usage: Annotate a research dataset or publication with Dublin Core terms including title, creator (author), date, subject (keywords), description, format (data type), identifier (DOI/URL), and rights (license) to enable standardized discovery and citation across digital repositories.

Metrics & Statistics

Graph Statistics

Total Nodes

296

Total Edges

632

Root Nodes

1

Leaf Nodes

210
Knowledge Coverage Statistics

Classes

11

Individuals

26

Properties

0
Hierarchical Metrics

Maximum Depth

1

Minimum Depth

0

Average Depth

0.50

Depth Variance

0.25
Breadth Metrics

Maximum Breadth

1

Minimum Breadth

1

Average Breadth

1.00

Breadth Variance

0.00
LLMs4OL Dataset Statistics

Term Types

30

Taxonomic Relations

0

Non-taxonomic Relations

0

Average Terms per Type

3.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import DublinCore

ontology = DublinCore()
ontology.load("path/to/DublinCore-ontology.rdf")

# Extract datasets
data = ontology.extract()

# Access specific relations
term_types = data.term_typings
taxonomic_relations = data.type_taxonomies
non_taxonomic_relations = data.type_non_taxonomic_relations