Not an Ontology

2025-05-26 · Source: Intentional Arrangement · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

Five major data platform products, including Databricks Genie Ontology, Palantir Foundry, Microsoft Fabric IQ, Snowflake Cortex Analyst, and Snowflake's Open Semantic Interchange (OSI), are critically assessed for their claims of providing "ontologies" in mid-2026. The analysis reveals that while these offerings address emerging enterprise demands for data sovereignty, persistent knowledge infrastructure, and computable meaning, none of them implement the core reasoning and inference capabilities inherent to a true ontology. Gartner forecasts worldwide sovereign cloud spending near \$80 billion in 2026, driven by data residency and compliance like the EU AI Act. Products like Genie, Foundry, and Fabric IQ rely on proprietary models, leading to vendor lock-in. In contrast, OSI, an open specification released v0.1.1 in December 2025, offers exportable semantic interchange, aligning closer to W3C standards like RDF and OWL, which are crucial for vendor-independent semantics and actual reasoning.

Key takeaway

For AI Architects evaluating data platforms for robust knowledge infrastructure, be wary of products marketed as "ontologies" that lack true reasoning and inference capabilities. Many current offerings, despite their claims, provide only retrieval or traversal, not the deep semantic understanding required for genuine data sovereignty and computable meaning. Prioritize solutions built on open standards like RDF and OWL, or those actively developing real ontological reasoning, to ensure your systems can derive new facts and avoid vendor lock-in.

Key insights

Major data platforms market "ontologies" that lack true reasoning and inference, failing to meet core definitional requirements.

Principles

• Ontologies fundamentally enable reasoning and inference over explicit conceptualizations.
• True data sovereignty relies on open, vendor-neutral standards like RDF and OWL.
• AI systems require symbolic structures for consistent, explainable knowledge.

In practice

• Utilize W3C standards (RDF, OWL) for portable, vendor-independent semantic models.
• Adopt open specifications like OSI for shared, exportable business definitions.
• Ground generative AI models in structured knowledge graphs to enhance factuality.

Topics

• Ontology
• Data Sovereignty
• Knowledge Infrastructure
• Semantic Web Standards
• AI Reasoning
• Open Semantic Interchange

Code references

• open-semantic-interchange/OSI

Best for: Investor, CTO, VP of Engineering/Data, AI Scientist, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Intentional Arrangement.