Dislocation Simulation and Model Ontology (DSIM)

The Dislocation Simulation and Model Ontology (DSIM) is a domain ontology developed to model concepts and relationships in the field of discrete dislocation dynamics and microscopy techniques used in dislocation research. DSIM provides a structured vocabulary for representing numerical representations of dislocations in simulations, as well as pictorial concepts such as pixels in experimental images (e.g., TEM, SEM, FIM). The ontology enables semantic annotation of simulation workflows, experimental setups, and image analysis procedures, supporting data integration and reproducibility in materials science. DSIM is designed for extensibility, allowing researchers to describe new simulation methods, image processing techniques, and dislocation phenomena. By providing a standardized framework, DSIM facilitates cross-study comparison, advanced analytics, and knowledge sharing in dislocation research. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community.

Example Usage: Annotate a dislocation dynamics simulation with DSIM terms to specify the simulation method, dislocation types, image analysis workflow, and experimental conditions, enabling semantic search and integration with microscopy data.

Metrics & Statistics

Graph Statistics

Total Nodes

313

Total Edges

673

Root Nodes

19

Leaf Nodes

119

Knowledge Coverage Statistics

Classes

47

Individuals

0

Properties

78

Hierarchical Metrics

Maximum Depth

7

Minimum Depth

0

Average Depth

2.05

Depth Variance

3.85

Breadth Metrics

Maximum Breadth

19

Minimum Breadth

1

Average Breadth

7.62

Breadth Variance

29.98

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

51

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 DSIM

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