Open Innovation Environment Characterisation (OIECharacterisation)¶
The Open Innovation Environment Characterisation (OIECharacterisation) ontology is an EMMO-compliant, domain-level ontology developed to represent characterisation methods in materials science [1] [2]. It provides a structured vocabulary for describing characterisation-method concepts and supports their alignment with the wider EMMO ontology ecosystem [1] [2].
OIECharacterisation is part of the Open Innovation Environment (OIE) ontology set, which covers characterisation methods, manufacturing processes, materials, models, and software products [1] [2]. The OIE ontologies are aligned with EMMO and were developed in the context of the OYSTER project [1] [2]. By providing a standardized semantic framework, OIECharacterisation supports semantic annotation, interoperability, data integration, and reuse of characterisation-related materials science information [1] [2].
Example Usage: Annotate a materials characterisation dataset with OIECharacterisation terms to specify characterisation methods, related measurement information, and links to EMMO-aligned materials science concepts, enabling semantic search and integration with materials informatics platforms [1] [2].
Metrics & Statistics¶
Total Nodes |
54 |
Total Edges |
135 |
Root Nodes |
1 |
Leaf Nodes |
11 |
Classes |
42 |
Individuals |
0 |
Properties |
0 |
Maximum Depth |
1 |
Minimum Depth |
0 |
Average Depth |
0.88 |
Depth Variance |
0.11 |
Maximum Breadth |
7 |
Minimum Breadth |
1 |
Average Breadth |
4.00 |
Breadth Variance |
9.00 |
Term Types |
0 |
Taxonomic Relations |
41 |
Non-taxonomic Relations |
0 |
Average Terms per Type |
0.00 |
Usage Example¶
Use the following code to import this ontology programmatically:
from ontolearner.ontology import OIECharacterisation
ontology = OIECharacterisation()
ontology.load("path/to/OIECharacterisation-ontology.ttl")
# 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