Crystallographic Information Framework Core Dictionary (CIFCore)

The Crystallographic Information Framework Core Dictionary (CIFCore) is a domain ontology developed to provide a machine-actionable representation of data files covering various aspects of crystallography and related structural sciences. CIFCore explains the historical development of CIF dictionaries and demonstrates the handling of complex information types in crystallographic data. The ontology supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and advanced analysis in structural biology, materials science, and chemistry. CIFCore facilitates the standardized description of crystal structures, symmetry operations, atomic coordinates, and experimental conditions. By providing a comprehensive vocabulary, CIFCore supports data sharing, reproducibility, and computational modeling in crystallography research. The ontology is actively maintained and extended to incorporate new crystallographic concepts and data standards.

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

Metrics & Statistics

Graph Statistics

Total Nodes

4494

Total Edges

15377

Root Nodes

1

Leaf Nodes

3310

Knowledge Coverage Statistics

Classes

1182

Individuals

0

Properties

0

Hierarchical Metrics

Maximum Depth

1

Minimum Depth

0

Average Depth

0.75

Depth Variance

0.19

Breadth Metrics

Maximum Breadth

3

Minimum Breadth

1

Average Breadth

2.00

Breadth Variance

1.00

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

27150

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 CIFCore

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