Open Innovation Environment Materials (OIEMaterials)¶
The Open Innovation Environment Materials (OIEMaterials) ontology is a domain-level ontology developed to represent materials in materials science. It provides a structured vocabulary for describing materials, their properties, and data, supporting both experimental and computational research in materials science.
The ontology employs a class-based modeling approach, defining classes for different types of materials, properties, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEMaterials supports the integration of data from various sources, promoting interoperability and data-driven research in materials science.
Typical applications of OIEMaterials include the development of new materials with specific properties, the optimization of material properties, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEMaterials enhances collaboration and innovation in the field of materials science.
Example Usage: Annotate a materials dataset with OIEMaterials terms to specify material types, properties, and data, enabling semantic search and integration with materials informatics platforms.
Metrics & Statistics¶
Total Nodes |
278 |
Total Edges |
561 |
Root Nodes |
13 |
Leaf Nodes |
115 |
Classes |
119 |
Individuals |
0 |
Properties |
0 |
Maximum Depth |
4 |
Minimum Depth |
0 |
Average Depth |
1.64 |
Depth Variance |
1.87 |
Maximum Breadth |
14 |
Minimum Breadth |
6 |
Average Breadth |
10.00 |
Breadth Variance |
10.00 |
Term Types |
0 |
Taxonomic Relations |
156 |
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 OIEMaterials
ontology = OIEMaterials()
ontology.load("path/to/OIEMaterials-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