Establishing a central knowledge architecture for vehicle data.
An overarching semantic model connects vehicle structures, components and attributes and makes product knowledge available consistently for domains, applications and AI.
The question
How does fragmented vehicle knowledge become usable company-wide when inconsistent data models require individual pipelines and complex integrations for every application?
Our answer
A central ontology represents vehicle types, configurations, components and attributes in a unified semantic model. Existing data structures are harmonised in a knowledge graph and made accessible for domains, applications and AI through automated data pipelines, standardised interfaces and flexible queries.
What we delivered
A semantic model for all vehicle product data
Vehicle types, configurations, components and attributes unambiguously defined and linked with one another
Fragmented data structures connected into vehicle knowledge
Existing models harmonised and their relationships represented in a shared knowledge architecture
Product knowledge made accessible flexibly and reusably
Automated pipelines, standardised interfaces and flexible queries provided for different usage scenarios
What it achieved
- Unified vehicle knowledge replaces application-specific data preparation and integrations
- Standardised interfaces accelerate new applications and data products
- Consistent product data reduces contradictions between systems and domains
- Semantic access creates a robust basis for AI applications
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