Going Meta: A Season of Building (and Grading) Ontologies

Neo4j’s Going Meta Season 3 examined how ontologies can support the creation, validation, governance, memory, and actions of AI agents. Episodes demonstrated automated ontology development using Neo4j’s data modeling MCP server and agent skills, with competency questions, source data, reusable models, visualizations, OWL/Turtle serialization, and iterative quality checks. The season also challenged the idea that ontologies alone prevent LLM hallucinations, finding that plain-language descriptions can perform as well as formal ontology serializations and emphasizing post-generation schema validation. It introduced ontology-quality measures covering structural, requirement-alignment, pragmatic, and logical criteria, alongside SHACL-derived graph validation reports. Other episodes showed ontologies enabling generic agent tools, shaping short-term, long-term, and reasoning memory, preserving ontology-design decisions, and improving extraction from conversations through configurable NLP, local-model, and LLM pipelines. The final episode explored graph-structured agent skills and Neo4j’s NAMS service, which can distill successful agent interactions into versioned, auditable skills, detect drift, and reduce token use. Season 4 is planned to expand from individual ontology techniques to the broader AI-and-ontologies landscape.

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