SciKnowOrg/ontolearner-materials_science_and_engineering

Materials Science And Engineering Domain Ontologies

Overview

Materials Science and Engineering is a multidisciplinary domain that focuses on the study and application of materials, emphasizing their structure, properties, processing, and performance in engineering contexts. This field is pivotal for advancing knowledge representation, as it integrates principles from physics, chemistry, and engineering to innovate and optimize materials for diverse technological applications. By systematically categorizing and modeling material-related data, this domain facilitates the development of new materials and enhances the understanding of their behavior under various conditions.

Ontology ID	Full Name	Classes	Properties	Last Updated
BattINFO	Battery Interface Ontology (BattINFO)	4431	304	nan
SSN	Semantic Sensor Network Ontology (SSN)	22	38	2017-04-17
EMMO	The Elementary Multiperspective Material Ontology (EMMO)	2448	181	2024-03
PRIMA	PRovenance Information in MAterials science (PRIMA)	67	67	2024-01-29
CIFCore	Crystallographic Information Framework Core Dictionary (CIFCore)	1182	0	May 24, 2023
LPBFO	Laser Powder Bed Fusion Ontology (LPBFO)	508	38	2022-09-20
MDSOnto	The Modular Ontology for Materials and Data Science (MDS-Onto)	1710	169	2026-02-03
VIMMP	Virtual Materials Marketplace Ontologies (VIMMP)	1234	771	2021-01-02
MOLBRINELL	MatoLab Brinell Test Ontology (MOL_BRINELL)	37	21	05/05/2022
MatOnto	Material Ontology (MatOnto)	1307	95	nan
PMDco	The Platform MaterialDigital core ontology (PMDco)	1002	66	2025-03-20
MatWerk	NFDI MatWerk Ontology (MatWerk)	449	129	2025-03-01
MaterialInformation	Material Information Ontology (MaterialInformation)	548	98	nan
MOLTENSILE	Matolab Tensile Test Ontology (MOL_TENSILE)	371	95	04/16/2021
AMOntology	Additive Manufacturing Ontology (AMOntology)	328	21	2023-05-10
MMO	Materials Mechanics Ontology (MMO)	428	17	2024-01-30
MechanicalTesting	Mechanical Testing Ontology (MechanicalTesting)	369	5	nan
CHAMEO	Characterisation Methodology Domain Ontology (CHAMEO)	203	52	2024-04-12
MDS	Materials Data Science Ontology (MDS)	363	10	03/24/2024
PeriodicTable	Periodic Table of the Elements Ontology (PeriodicTable)	6	13	2004/02/05
BVCO	Battery Value Chain Ontology (BVCO)	262	6	nan
ASMO	Atomistic Simulation Methods Ontology (ASMO)	99	41	nan
NanoMine	NanoMine Ontology (NanoMine)	157	0	nan
OIEManufacturing	Open Innovation Environment Manufacturing (OIEManufacturing)	222	3	nan
MSEO	Materials Science and Engineering Ontology (MSEO)	138	2	nan
MAT	Material Properties Ontology (MAT)	140	21	nan
GPO	General Process Ontology (GPO)	187	17	nan
DISO	Dislocation Ontology (DISO)	62	45	21.03.202
MSLE	Material Science Lab Equipment Ontology (MSLE)	45	10	Sep 15, 2022
DSIM	Dislocation Simulation and Model Ontology (DSIM)	47	78	17.08.2023
ONTORULE	Ontology for the Steel Domain (ONTORULE)	24	37	2010-05-31
OIEMaterials	Open Innovation Environment Materials (OIEMaterials)	119	0	nan
OIESoftware	Open Innovation Environment Software (OIESoftware)	155	0	nan
MAMBO	Molecules And Materials Basic Ontology (MAMBO)	57	104	nan
CMSO	Computational Material Sample Ontology (CMSO)	45	51	nan
EMMOCrystallography	Crystallography Ontology (EMMOCrystallography)	61	5	nan
MicroStructures	EMMO-based ontology for microstructures (MicroStructures)	43	0	nan
OIEModels	Open Innovation Environment Models (OIEModels)	108	1	nan
SystemCapabilities	System Capabilities Ontology (SystemCapabilities)	25	8	2017-05-14
PLDO	Planar Defects Ontology (PLDO)	27	15	nan
Photovoltaics	EMMO Domain Ontology for Photovoltaics (Photovoltaics)	47	3	nan
BMO	Building Material Ontology (BMO)	24	62	2019-12-10
FSO	Flow Systems Ontology (FSO)	14	22	2020-08-06
PODO	Point Defects Ontology (PODO)	12	5	nan
LDO	Line Defect Ontology (LDO)	30	11	nan
MatVoc	Materials Vocabulary (MatVoc)	28	15	2022-12-12
OIECharacterisation	Open Innovation Environment Characterisation (OIECharacterisation)	42	0	nan
HPOnt	The Heat Pump Ontology (HPOnt)	4	12	nan
MDO	Materials Design Ontology (MDO)	13	13	2022-08-02
CDCO	Crystallographic Defect Core Ontology (CDCO)	7	2	nan
Atomistic	Atomistic Ontology (Atomistic)	12	2	nan

Dataset Files

Each ontology directory contains the following files:

1. <ontology_id>.<format> - The original ontology file
2. term_typings.json - Dataset of term to type mappings
3. taxonomies.json - Dataset of taxonomic relations
4. non_taxonomic_relations.json - Dataset of non-taxonomic relations
5. <ontology_id>.rst - Documentation describing the ontology

Usage

These datasets are intended for ontology learning research and applications. Here's how to use them with OntoLearner:

First of all, install the OntoLearner library via PiP:

pip install ontolearner

How to load an ontology or LLM4OL Paradigm tasks datasets?

from ontolearner import BattINFO

ontology = BattINFO()

# Load an ontology.
ontology.load()

# Load (or extract) LLMs4OL Paradigm tasks datasets
data = ontology.extract()

How use the loaded dataset for LLM4OL Paradigm task settings?

# Import core modules from the OntoLearner library
from ontolearner import BattINFO, LearnerPipeline, train_test_split

# Load the BattINFO ontology, which contains concepts related to wines, their properties, and categories
ontology = BattINFO()
ontology.load()  # Load entities, types, and structured term annotations from the ontology
ontological_data = ontology.extract()

# Split instances into train and test sets
train_data, test_data = train_test_split(ontological_data, test_size=0.2, random_state=42)

# Initialize a multi-component learning pipeline (retriever + LLM)
# This configuration enables a Retrieval-Augmented Generation (RAG) setup
pipeline = LearnerPipeline(
    retriever_id='sentence-transformers/all-MiniLM-L6-v2',      # Dense retriever model for nearest neighbor search
    llm_id='Qwen/Qwen2.5-0.5B-Instruct',                        # Lightweight instruction-tuned LLM for reasoning
    hf_token='...',                                             # Hugging Face token for accessing gated models
    batch_size=32,                                              # Batch size for training/prediction if supported
    top_k=5                                                     # Number of top retrievals to include in RAG prompting
)

# Run the pipeline: training, prediction, and evaluation in one call
outputs = pipeline(
    train_data=train_data,
    test_data=test_data,
    evaluate=True,              # Compute metrics like precision, recall, and F1
    task='term-typing'          # Specifies the task
                                # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
)

# Print final evaluation metrics
print("Metrics:", outputs['metrics'])

# Print the total time taken for the full pipeline execution
print("Elapsed time:", outputs['elapsed_time'])

# Print all outputs (including predictions)
print(outputs)

For more detailed documentation, see the

Citation

If you find our work helpful, feel free to give us a cite.

@inproceedings{babaei2023llms4ol,
  title={LLMs4OL: Large language models for ontology learning},
  author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{"o}ren},
  booktitle={International Semantic Web Conference},
  pages={408--427},
  year={2023},
  organization={Springer}
}