EUropean Research Information Ontology (EURIO)

The EUropean Research Information Ontology (EURIO) conceptualizes, formally encodes, and makes available data about research projects funded by the European Union’s framework programmes for research and innovation in an open, structured, and machine-readable format [1] [2]. EURIO was developed for CORDIS and the Publications Office of the European Union to represent research project information as semantic data, improving its visibility, accessibility, interoperability, and reuse [2]. It provides a structured vocabulary for describing research projects, funding schemes, grants, organizations, persons, project results, publications, and related research information [1] [2].

The ontology uses a class-based semantic modeling approach, defining classes and properties for research projects, participants, funding information, outputs, and administrative metadata [2]. These concepts allow EU-funded research information to be organized, linked, queried, and integrated with other semantic resources and reference data assets [1] [2]. EURIO supports interoperability in research information management by enabling project data from CORDIS and related sources to be represented consistently in linked data formats [1].

Typical applications of EURIO include semantic publication of EU research project data, research information management, project discovery, funding analysis, institutional reporting, knowledge graph construction, and integration of research outputs across platforms [2]. By providing a standardized ontology for EU-funded research information, EURIO supports data discovery, analytics, interoperability, and knowledge sharing across research information systems [1] [2].

Example Usage: Annotate an EU-funded research project with EURIO terms to specify the project title, acronym, grant information, funding programme, participating organizations, researchers, project duration, deliverables, publications, and related results. This enables semantic search, project discovery, funding analysis, and integration with research information management platforms [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

502

Total Edges

1193

Root Nodes

18

Leaf Nodes

204

Knowledge Coverage Statistics

Classes

44

Individuals

0

Properties

111

Hierarchical Metrics

Maximum Depth

14

Minimum Depth

0

Average Depth

6.54

Depth Variance

11.75

Breadth Metrics

Maximum Breadth

56

Minimum Breadth

4

Average Breadth

24.73

Breadth Variance

192.33

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

43

Non-taxonomic Relations

4

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import EURIO

ontology = EURIO()
ontology.load("path/to/EURIO-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

References