Line Defect Ontology (LDO)

The Line Defect Ontology (LDO) is a domain ontology developed to provide a comprehensive and standardized vocabulary for describing line defects in crystalline materials, such as dislocations and disclinations. LDO enables the semantic annotation of experimental and computational data related to line defects, supporting interoperability and data integration across materials science databases and research platforms. The ontology covers key concepts including defect types, geometric and topological properties, formation mechanisms, and interactions with other defects or microstructural features. LDO is designed for extensibility, allowing researchers to describe new line defect types, characterization methods, and material systems as the field evolves. By providing a rigorous semantic framework, LDO facilitates advanced analytics, defect modeling, and knowledge sharing in materials science and engineering. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community.

Example Usage: Annotate a transmission electron microscopy (TEM) dataset with LDO terms to specify the types of line defects observed, their Burgers vectors, line directions, and interactions with grain boundaries, enabling semantic search and integration with defect modeling tools.

Metrics & Statistics

Graph Statistics

Total Nodes

111

Total Edges

207

Root Nodes

6

Leaf Nodes

49
Knowledge Coverage Statistics

Classes

30

Individuals

0

Properties

11
Hierarchical Metrics

Maximum Depth

4

Minimum Depth

0

Average Depth

1.25

Depth Variance

1.56
Breadth Metrics

Maximum Breadth

6

Minimum Breadth

1

Average Breadth

3.20

Breadth Variance

2.96
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

21

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 LDO

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