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SHACL

6 articles · Page 1 of 1
Data Bricklaying Diary
Data Bricklaying Diary
Oct 21, 2026 · Artificial Intelligence

AI-Generated Ontologies: What Experts Must Review Beyond Syntax

This article outlines a structured review process for AI-generated ontology drafts, emphasizing that experts must verify business meaning, evidence sources, and high-risk judgments — not just technical correctness — and demonstrates how to trace rejections, revisions, and version-specific approvals using a supply-chain quality case study.

AI-generated ontologyOntologySHACL
0 likes · 17 min read
AI-Generated Ontologies: What Experts Must Review Beyond Syntax
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Oct 20, 2026 · Industry Insights

Why No Chinese Company Has Built a Real Ontology Product: The 10-System Gap

The article explains why Chinese ontology product companies fail: a 5-month, 10-person demo only takes the shortest path for each of ten required systems — data ingestion, mapping, modeling, standards, versioning, collaboration, quality, reasoning, operations, and delivery — while a production product needs all ten fully built and integrated, a challenge that demands a full-stack architect plus AI leverage.

GraphRAGKnowledge GraphOWL
0 likes · 10 min read
Why No Chinese Company Has Built a Real Ontology Product: The 10-System Gap
Data Bricklaying Diary
Data Bricklaying Diary
Sep 24, 2026 · Artificial Intelligence

Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer

This article uses an order management example to explain why a knowledge graph alone cannot capture business meaning, defines ontology as explicit computer-processable descriptions of concepts and constraints, distinguishes ontology from knowledge graphs and graph databases, compares OWL and OPM modeling approaches, and provides six validation questions for ontology-driven data governance.

Graph DatabaseKnowledge GraphOPM
0 likes · 21 min read
Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 3, 2026 · Industry Insights

OntoL: Ontology Modeling & Semantic Layer for Enterprise Knowledge Governance

This article analyzes OntoL, an ontology modeling tool for enterprise knowledge governance, detailing its three-layer architecture, core capabilities like visual modeling and versioning, applicable scenarios, current technical limitations, and future roadmap including SHACL constraints and federated query.

Knowledge GovernanceKnowledge GraphOWL
0 likes · 9 min read
OntoL: Ontology Modeling & Semantic Layer for Enterprise Knowledge Governance
Data Bricklaying Diary
Data Bricklaying Diary
Aug 24, 2026 · Fundamentals

Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily

This article explains that ontology modeling uses familiar business objects, attributes, relationships, and constraints from daily data and process work, but adds cross-system unified semantics, explicit computable constraints, machine verification, and continuous operations to enable data, rules, systems, and AI agents to collaborate on a shared business world.

AI readinessEnterprise ArchitectureOWL
0 likes · 20 min read
Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily