.. sidebar:: .. list-table:: **Ontology Card** :header-rows: 0 * - **Domain** - Materials Science and Engineering * - **Category** - Materials Science * - **Current Version** - 0.4.3 * - **Last Updated** - None * - **Creator** - Lukas Gold, Simon Stier * - **License** - Creative Commons Attribution 4.0 International (CC BY 4.0) * - **Format** - ttl * - **Download** - `Download Battery Value Chain Ontology (BVCO) `_ Battery Value Chain Ontology (BVCO) ======================================================================================================== The Battery Value Chain Ontology (BVCO) is a domain ontology developed to model processes, entities, and relationships along the battery value chain, from raw material extraction to recycling and disposal. BVCO provides a structured vocabulary for describing holistic processes that transform inputs (matter, energy, information) into outputs (products, byproducts, waste) using tools such as devices and algorithms. The ontology supports decomposition of processes into sub-processes, capturing predecessor and successor relationships, and enabling detailed modeling of manufacturing, logistics, usage, and end-of-life stages. BVCO facilitates semantic annotation of battery value chain data, supporting interoperability, data integration, and advanced analytics across research, industry, and regulatory platforms. By providing a standardized framework, BVCO enables lifecycle assessment, supply chain optimization, and sustainability analysis in the battery industry. The ontology is actively maintained and extended to incorporate new battery technologies, process innovations, and regulatory requirements. **Example Usage**: Annotate a battery manufacturing workflow with BVCO terms to specify raw material sourcing, cell assembly, quality control, logistics, and recycling processes, enabling semantic search and integration with supply chain management systems. Metrics & Statistics -------------------------- .. tab:: Graph .. list-table:: Graph Statistics :widths: 50 50 :header-rows: 0 * - **Total Nodes** - 804 * - **Total Edges** - 1719 * - **Root Nodes** - 85 * - **Leaf Nodes** - 283 :: .. tab:: Coverage .. list-table:: Knowledge Coverage Statistics :widths: 50 50 :header-rows: 0 * - **Classes** - 262 * - **Individuals** - 0 * - **Properties** - 6 :: .. tab:: Hierarchy .. list-table:: Hierarchical Metrics :widths: 50 50 :header-rows: 0 * - **Maximum Depth** - 14 * - **Minimum Depth** - 0 * - **Average Depth** - 2.47 * - **Depth Variance** - 5.27 :: .. tab:: Breadth .. list-table:: Breadth Metrics :widths: 50 50 :header-rows: 0 * - **Maximum Breadth** - 230 * - **Minimum Breadth** - 2 * - **Average Breadth** - 52.20 * - **Breadth Variance** - 4920.43 :: .. tab:: LLMs4OL .. list-table:: LLMs4OL Dataset Statistics :widths: 50 50 :header-rows: 0 * - **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: .. code-block:: python 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