Materials Vocabulary (MatVoc)

The Materials Vocabulary (MatVoc) is an ontology developed within the STREAM project to provide a structured vocabulary for representing materials and their properties. It aims to facilitate the integration and retrieval of materials data across various domains, supporting applications in materials science, engineering, and related fields. MatVoc encompasses key entities such as materials, properties, and processes, and models relationships to capture the complexity of materials data.

The ontology employs a class-based modeling approach, defining classes for different types of materials and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MatVoc supports the integration of data from experimental studies, computational simulations, and industrial applications, promoting interoperability and data-driven research in materials science.

Typical applications of MatVoc include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MatVoc enhances collaboration and innovation in the field of materials science.

Example Usage: Annotate a materials database with MatVoc terms to specify material types, properties, and processes, enabling semantic search and integration with materials informatics platforms.

Metrics & Statistics

Graph Statistics

Total Nodes

94

Total Edges

161

Root Nodes

16

Leaf Nodes

44
Knowledge Coverage Statistics

Classes

28

Individuals

0

Properties

15
Hierarchical Metrics

Maximum Depth

2

Minimum Depth

0

Average Depth

0.62

Depth Variance

0.48
Breadth Metrics

Maximum Breadth

16

Minimum Breadth

4

Average Breadth

10.67

Breadth Variance

24.89
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

6

Non-taxonomic Relations

7

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import MatVoc

ontology = MatVoc()
ontology.load("path/to/MatVoc-ontology.rdf")

# 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