Unlocking Advanced Knowledge Management
A Multi-Axial Mindset for Ontology Design
This paper analyzes Wikidata's unique multi-axial, polyhierarchical ontology design, contrasting it with traditional foundational ontologies. It highlights how Wikidata accommodates multiple classification axes simultaneously under a shared root, enabling flexible and modular knowledge graph construction suitable for collaborative and evolving environments. Despite its structural capacity, current usage shows inconsistency and limited coverage of these axes, indicating a need for better curation and expansion of principled primary splits.
Executive Impact & Key Findings
Wikidata's multi-axial approach offers a scalable and modular blueprint for enterprise knowledge graphs. Below are key insights into its structural capacity and current application.
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Entities in Wikidata
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Multi-Axial Classes Identified
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Key Classification Axes
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Ontology coverage by Abstract/Concrete axis
Wikidata's Polyhierarchy at Root (Simplified)
Entity (Q35120)
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Abstract/Concrete Axis
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Named/Nameless Axis
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Individual/Collective Axis
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Observable/Unobservable Axis
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Human (Q5) as multi-axial instance
| Ontology | Primary Split(s) | Wikidata's Approach |
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| BFO |
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| Wikidata |
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Case Study: Human (Q5)
The class human (Q5) exemplifies multi-axial classification. It is simultaneously a subclass of concrete object, an individual entity, and ideally, an observable entity. This illustrates how Wikidata accommodates non-contradictory classifications from orthogonal axes, reflecting its pluralist design without privileging a single top-level distinction.
Case Study: Painting (Q3305213)
The class painting (Q3305213) demonstrates Wikidata's tolerance for partial classification. It is not explicitly typed under Abstract/Concrete or Named/Nameless, as its class generalizes across these features. This approach prioritizes accuracy and applicability over abstract completeness, enabling instance-level exhaustiveness without enforcing subclass duplication at the class level.
Advanced ROI Calculator
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Your Multi-Axial Ontology Roadmap
A phased approach to integrate multi-axial ontology design principles into your enterprise knowledge management strategy.
Phase 1: Foundational Assessment
Evaluate existing knowledge structures and identify core domains that could benefit from a multi-axial approach.
Phase 2: Multi-Axial Schema Design
Develop and prototype multi-axial classification schemas for identified domains, focusing on orthogonality and modularity.
Phase 3: Phased Integration & Data Migration
Incrementally integrate new multi-axial classifications into your knowledge graph and migrate existing data, ensuring data consistency.
Phase 4: Governance & Iterative Refinement
Establish clear governance policies for multi-axial ontology evolution and set up iterative refinement processes for continuous improvement and community engagement.
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