The Modular Ontology for Materials and Data Science (MDS-Onto)

MDS-Onto is a domain-level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Each module contains multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, and Accelerated Exposure, providing a comprehensive framework for representing data and processes in materials science.

MDS-Onto employs a modular approach, defining classes and properties for each module to capture the complexity of materials data science. It supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases and digital platforms. By providing a standardized framework, MDS-Onto facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry.

Typical applications include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements.

Example Usage: Annotate a materials database with MDS-Onto terms to specify material properties, processes, and data, enabling semantic search and integration with materials informatics platforms.

Metrics & Statistics

Graph Statistics

Total Nodes

4362

Total Edges

13095

Root Nodes

92

Leaf Nodes

2226
Knowledge Coverage Statistics

Classes

1710

Individuals

1

Properties

169
Hierarchical Metrics

Maximum Depth

7

Minimum Depth

0

Average Depth

1.03

Depth Variance

2.01
Breadth Metrics

Maximum Breadth

92

Minimum Breadth

1

Average Breadth

25.75

Breadth Variance

1093.94
LLMs4OL Dataset Statistics

Term Types

1

Taxonomic Relations

1648

Non-taxonomic Relations

259

Average Terms per Type

1.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import MDSOnto

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