Curated papers, guides, tools, and communities for mastering semantic AI, knowledge graphs, and data infrastructure.
The definitive textbook on building ontologies with RDF, OWL, and SPARQL. Essential reading for anyone building semantic infrastructure.
The transformer paper that underpins modern semantic embeddings. Understanding this is foundational for semantic AI.
The official MCP spec — the emerging standard for how AI agents access structured data. Critical reading for 2026 AI architects.
Comprehensive documentation for defining metrics as code using MetricFlow. The most developer-friendly semantic layer available.
The foundational paper for contextual word embeddings. Explains how modern semantic similarity is computed.
The original RAG paper. Essential for understanding how semantic retrieval combines with language model generation.
The world's largest open knowledge graph. A practical resource for understanding real-world ontology at scale.
The leading library for computing semantic embeddings. Use this to build production-grade semantic similarity systems.
The official W3C specification for OWL 2. The formal standard for semantic web ontologies.
The leading vector database for semantic search at scale. Essential infrastructure for RAG and semantic similarity applications.
A practical guide to building a semantic layer with Cube — covering data modeling, API design, and AI integration.
The foundational text on linked data principles. Explains the vision behind semantic web technologies.