Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)

2026-05-23 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The "ontological continuum" is introduced as a theoretical construct to address the challenges of diverse knowledge graph (KG) modeling practices, which make integration and reuse expensive in modern AI, particularly for neuro-symbolic systems. While Generative AI offers automation, a principled understanding of the KG space is lacking. This continuum is defined by two orthogonal distinctions: semantics vs pragmatics, and properties vs affordances, providing a vocabulary to describe, compare, navigate, and transform KGs. The methodology is empirical, aiming to define a theory of existing KG engineering practices, which can be formally explicit through techniques like Formal Concept Analysis (FCA). A case study on provenance knowledge illustrates how a single concern varies across the continuum, and the authors outline five open research challenges for community development.

Key takeaway

For Knowledge Engineers integrating diverse knowledge graphs or developing neuro-symbolic AI systems, understanding the "ontological continuum" is crucial. This framework provides a principled vocabulary to describe, compare, and transform KGs, addressing the brittleness of current integration practices. You should consider applying its distinctions of semantics/pragmatics and properties/affordances to analyze your existing KG assets and guide re-engineering efforts, potentially contributing to its ongoing development.

Key insights

The "ontological continuum" offers a principled framework to describe and transform diverse knowledge graph modeling practices.

Principles

• Knowledge graph diversity complicates integration and reuse.
• KG re-engineering is critical for neuro-symbolic AI.
• Empirical observation informs theory of existing KG practices.

Method

The method involves empirically deriving a theory of existing KG engineering practices, formalizing its structure using techniques like Formal Concept Analysis (FCA).

In practice

• Describe and compare diverse knowledge graph models.
• Navigate and transform KGs for new requirements.
• Analyze provenance knowledge across different KG representations.

Topics

• Knowledge Graphs
• Ontological Continuum
• Neuro-symbolic AI
• Generative AI
• Data Integration
• Formal Concept Analysis

Best for: Research Scientist, AI Scientist, AI Architect

Related on AIssential

• What Makes Knowledge Graphs AI-Ready — Knowledge Graphs for AI bridge siloed data semantics with organizational ontology for machine-readable reasoning.
• When Chloe Became a Better Ontologist Than Me — AI collaborators can organically design ontologies and trivialise complex knowledge graph integration, shifting human roles to judgment.
• The KG-ER Conceptual Schema Language — KG-ER provides a representation-agnostic conceptual schema language for knowledge graphs, enhancing semantic capture.
• The Gap That Runs Both Ways — RDF 1.2 reification enables formal systems to model speech acts, bridging the expressivity gap with natural language by adding precision and provenance.
• The Ontology Pipeline™, Refresh — Effective semantic knowledge infrastructure requires a disciplined, sequential approach, augmented by AI and robust governance.
• Ontology, Part I — Conflicting definitions and terminology create significant barriers to understanding and implementing ontologies in AI.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.