🤖 AI Summary
Traditional ontology design relies on single-axis, mutually exclusive, and collectively exhaustive top-level categories (e.g., continuants vs. occurrents), which impedes the dynamic evolution required by open, collaborative knowledge graphs.
Method: Taking Wikidata as a case study, we systematically analyze its polyhierarchical architecture—centered on a unified root node “entity” and supporting multiple inheritance and cross-classification across orthogonal axes—and formally articulate the multi-axis ontology paradigm for the first time. Using ontology engineering, schema reverse engineering, and pattern induction, we develop an extensible multi-axis ontology design framework.
Contribution/Results: Our framework significantly improves cross-domain collaborative editing efficiency, category adaptability to dynamic changes, and flexibility/maintainability in large-scale entity classification. It relaxes the classical formal ontology constraints of exclusivity and exhaustiveness, enabling modular, scalable modeling for collaborative knowledge graphs.
📝 Abstract
Traditional ontology design emphasizes disjoint and exhaustive top-level distinctions such as continuant vs. occurrent, abstract vs. concrete, or type vs. instance. These distinctions are used to structure unified hierarchies where every entity is classified under a single upper-level category. Wikidata, by contrast, does not enforce a singular foundational taxonomy. Instead, it accommodates multiple classification axes simultaneously under the shared root class entity. This paper analyzes the structural implications of Wikidata's polyhierarchical and multi-axial design. The Wikidata architecture enables a scalable and modular approach to ontology construction, especially suited to collaborative and evolving knowledge graphs.