Battery Value Chain Ontology (BVCO)¶
The Battery Value Chain Ontology (BVCO) is a domain ontology developed to model processes along the battery value chain [2] [3]. BVCO provides a structured vocabulary for describing holistic processes that transform inputs or educts, such as matter, energy, and information, into outputs or products using tools such as devices and algorithms [2]. The ontology supports decomposition of processes into sub-processes and captures predecessor and successor relationships, enabling detailed representation of battery value chain activities [2].
BVCO facilitates semantic annotation of battery value chain data and supports interoperability, data integration, and knowledge sharing across battery research and industrial workflows [2] [3]. It is based on the General Process Ontology (GPO) and EMMO, while its authorship and development provenance are documented by Lukas Gold and Simon Stier [2] [1].
Example Usage: Annotate a battery manufacturing workflow with BVCO terms to specify raw material processing, cell production, quality-control steps, logistics, and recycling processes, enabling semantic search and integration with battery value chain data platforms [2] [3].
Metrics & Statistics¶
Total Nodes |
804 |
Total Edges |
1719 |
Root Nodes |
85 |
Leaf Nodes |
283 |
Classes |
262 |
Individuals |
0 |
Properties |
6 |
Maximum Depth |
14 |
Minimum Depth |
0 |
Average Depth |
2.47 |
Depth Variance |
5.27 |
Maximum Breadth |
230 |
Minimum Breadth |
2 |
Average Breadth |
52.20 |
Breadth Variance |
4920.43 |
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 BVCO
ontology = BVCO()
ontology.load("path/to/BVCO-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