Knowledge Graph Re-engineering Along the Ontological Continuum

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Knowledge graphs have become the primary vehicle for data integration and are critical to modern AI. Yet, the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle. This challenge is particularly acute for GenAI-driven automation of knowledge engineering, which navigates the KG space without precise directions. We introduce the ontological continuum: a task-relative navigation framework defined by two orthogonal distinctions: semantics versus pragmatics, and properties versus affordances, yielding four dimensions along which KGs can be described, compared, and transformed. Our methodological stance is empirical: we propose the ontological continuum as a theory of the existent, derived from observation of real-world KG engineering practices, whose structure can be made formally explicit, for example, through Formal Concept Analysis (FCA). We ground the vision in a case study on provenance knowledge and articulate five open research challenges for the community.