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KaifAhmad1 bdd99f7924 feat(cookbook): add Regulatory Intelligence use case

Adds an end-to-end cookbook use case that turns 9 real US federal
AI-governance and cybersecurity-regulation documents into an
explainable, ontology-driven knowledge graph: ingestion, chunking,
entity/relation/triplet extraction, ontology import/generation/
evaluation (6 vendored real W3C ontologies plus SKOS taxonomy),
entity resolution, SHACL validation, deterministic reasoning, PROV-O
provenance, an Oxigraph-backed persistent RDF store, conflict
detection, temporal reasoning, SPARQL, JSON-LD, GraphRAG retrieval,
and a five-agent Decision Intelligence workflow.

Real library rough edges hit along the way (noisy extraction over
dense prose, EntityResolver's batch merge not firing, the stub
OntologyValidator, find_precedents_advanced()'s vector-store bug, and
two VectorStore/HybridSearch bugs that drop metadata or crash for
non-inmemory backends) are reported honestly in the notebook output
and README rather than hidden.

2026-08-05 00:08:03 +05:30

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Use Cases

Self-contained, end-to-end examples that combine multiple Semantica modules to solve a real-world problem, built from real public data and real external ontologies rather than synthetic samples. Unlike the tutorials in introduction/ and advanced/, each use case is a folder, not a single notebook, with its own data/ (real source documents plus a download script) and ontology/ (vendored real ontologies plus a small domain extension) alongside the notebook itself.

Available Use Cases

Regulatory Intelligence. Turns 9 real U.S. federal AI-governance and cybersecurity-regulation documents (NIST AI RMF, NIST CSF 1.1/2.0, HIPAA Security Rule, Executive Order 14110, OMB M-24-10, and more) into an explainable, ontology-driven knowledge graph. Full pipeline: ingestion (PDFParser/DoclingParser), chunking (TextSplitter), automatic entity, relation, and triplet extraction across the corpus, ontology import, generation, and evaluation, entity resolution, graph construction (GraphBuilder), SHACL validation, deterministic rule-based reasoning (Reasoner), PROV-O provenance, a persistent RDF database (Oxigraph on disk, plus Semantica's TripletStore for a production server), conflict detection, temporal reasoning, SPARQL, JSON-LD, GraphRAG, and a five-agent Decision Intelligence workflow (precedent search, causal-chain interpretation, policy gating, decision audit reports). Reuses real W3C ontologies (ORG, PROV-O, SKOS, DCAT, OWL-Time, FRBR) rather than inventing new ones.

Folder Convention

use_cases/<name>/
├── README.md           overview, architecture, data and ontology attribution, how to run
├── data/
│   ├── download_*.py    fetches real source documents from their official URLs
│   ├── raw/              the fetched documents, plus a source_manifest.json (real URLs, retrieval dates)
│   └── README.md         data dictionary and source attribution
├── ontology/
│   ├── download_*.py    fetches real external ontologies (vendored byte-for-byte)
│   ├── external/         the vendored real ontology files
│   ├── *.ttl             small hand-authored schema extensions, aligned to the vendored ontologies
│   └── README.md
└── notebook/
    └── *.ipynb           the end-to-end walkthrough

2.3 KiB Raw Blame History

Use Cases

Available Use Cases

Folder Convention

2.3 KiB

Raw Blame History