Crystallography Ontology (EMMOCrystallography)

The Crystallography Ontology (EMMOCrystallography) is a domain ontology based on the Elementary Multiperspective Material Ontology (EMMO) and the Crystallographic Information Framework (CIF) core dictionary. It provides a formal language for representing crystallographic concepts, including crystal structures, symmetry operations, atomic positions, and experimental conditions. EMMOCrystallography supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and advanced analysis in materials science and structural biology. The ontology facilitates the standardized description of crystallographic experiments, data processing workflows, and structural models. By providing a rigorous semantic framework, EMMOCrystallography enhances reproducibility, data sharing, and computational modeling in crystallography research. The ontology is actively maintained and extended to incorporate new crystallographic concepts and community requirements.

Example Usage: Annotate a crystallographic dataset with EMMOCrystallography terms to specify crystal lattice parameters, symmetry groups, atomic coordinates, and experimental metadata, enabling semantic search and integration with structural databases.

Metrics & Statistics

Graph Statistics

Total Nodes

337

Total Edges

586

Root Nodes

29

Leaf Nodes

166
Knowledge Coverage Statistics

Classes

61

Individuals

0

Properties

5
Hierarchical Metrics

Maximum Depth

14

Minimum Depth

0

Average Depth

5.20

Depth Variance

9.99
Breadth Metrics

Maximum Breadth

74

Minimum Breadth

1

Average Breadth

22.07

Breadth Variance

290.60
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

0

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 EMMOCrystallography

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