Dislocation Ontology (DISO)¶
The Dislocation Ontology (DISO) is a domain ontology that defines concepts and relationships related to linear defects in crystalline materials, especially dislocations [1] [2]. It provides a structured vocabulary for describing dislocation-related concepts, including dislocation structures, dislocation lines, Burgers vectors, crystal structures, and relationships between dislocation-domain entities [2].
DISO was developed using a top-down approach, starting from general concepts in the dislocation domain and then specializing them into more specific concepts [1] [2]. Version 1.1 adapts and extends DISO for the discrete dislocation dynamics domain by adding missing concepts, improving class definitions, exploring additional relationships, and aligning it with related ontologies such as EMMO and the Materials Design Ontology (MDO) [1]. By providing a standardized semantic representation, DISO supports annotation, interoperability, data integration, and reuse of dislocation-related experimental and simulation data [2].
Example Usage: Annotate a transmission electron microscopy (TEM) dataset or dislocation dynamics simulation with DISO terms to specify dislocation structures, Burgers vectors, line directions, crystal-structure context, and relationships between dislocation-domain entities, enabling semantic search and integration with materials science databases and simulation workflows [1] [2].
Metrics & Statistics¶
Total Nodes |
324 |
Total Edges |
739 |
Root Nodes |
9 |
Leaf Nodes |
91 |
Classes |
62 |
Individuals |
0 |
Properties |
45 |
Maximum Depth |
1 |
Minimum Depth |
0 |
Average Depth |
0.18 |
Depth Variance |
0.15 |
Maximum Breadth |
9 |
Minimum Breadth |
2 |
Average Breadth |
5.50 |
Breadth Variance |
12.25 |
Term Types |
0 |
Taxonomic Relations |
38 |
Non-taxonomic Relations |
6 |
Average Terms per Type |
0.00 |
Usage Example¶
Use the following code to import this ontology programmatically:
from ontolearner.ontology import DISO
ontology = DISO()
ontology.load("path/to/DISO-ontology.owl")
# 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