OntologiesFeatured
Ontologies for AI Agents
How to define the entities, relationships, and rules an agent reasons over, and how to hand that definition to the agent at runtime.
Founder and Lead Contributor
Rodolfo Vergara
Rodolfo Vergara started in SEO in 2012, when ranking a page meant understanding exactly how a machine read it. That question has followed him ever since.
He has run DMR Agency since 2017, building funnels and marketing automation for clients and for his own products. Most of that work comes down to reading data: what people search for, where they drop off, and which page or message moved the number.
He started working with large language models early, joining OpenAI's GPT-3 beta in 2020–21 while access was still by invitation. Today his work centers on AI agent architecture through Agentic Difference and its Agent Oracle framework: how agents get the context they need, how knowledge is structured so a model can use it, and how to tell when a system is reading it wrong.
He also built Search Diligently, a free scripture study platform whose structured knowledge layer is a working example of the ideas on this site.
InformationScience.ai is where he writes that work down.
Articles
Foundations
What is information science?
Information science is the study of how information is organized, described, stored, found, judged, and used. It is the discipline behind retrieval, ontologies, and evaluation in AI systems.
Guides
- Knowledge GraphsFeatured
Knowledge Graphs for Grounded Retrieval
Designing, populating, and querying a knowledge graph that makes retrieval traceable and lets a model reason across more than one hop.
- AI ArchitectureFeatured
The AI Stack, Layer by Layer
A reference map of the modern AI stack, from data sources through the knowledge layer to models, orchestration, and interfaces.
- Data ModelingComing soon
Data Modeling for AI Systems
Bringing data modeling discipline to features, documents, chunks, and embeddings so every part of the system agrees on what the data means.
- Intelligent AgentsComing soon
Agent Memory and State
What an agent should remember, for how long, where that memory lives, and how it stays consistent with the knowledge layer.
- AI InfrastructureComing soon
Inference Infrastructure
Serving, routing, caching, and observing model inference so the system stays reliable and the bill stays explainable.
- Intelligent AgentsComing soon
Instructional Design for AI Agents
Applying instructional design to how agents are briefed: objectives, scaffolding, worked examples, and assessment instead of ever-longer prompts.
- OntologiesComing soon
Taxonomies, Vocabularies, and Ontologies
Choosing the right level of structure for a domain, from a flat controlled vocabulary to a formal ontology, and knowing when to stop.