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Core Concepts

The fundamental ideas behind Semantica — explained plainly.

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Start with Getting Started for hands-on examples, then come back to this page for deeper understanding.

What is Semantica?

Semantica transforms unstructured data (documents, web pages, reports, databases) into knowledge graphs — structured representations that AI systems can query, reason about, and trace back to sources.

At its core, Semantica adds a context and intelligence layer on top of your existing AI stack: it doesn't replace LangChain, LlamaIndex, or your LLM provider — it makes their outputs accountable.

Knowledge Graphs

The foundation of everything in Semantica.

A knowledge graph stores information as:

  • Nodes (entities) — people, companies, locations, events, concepts
  • Edges (relationships) — works_for, located_in, founded_by
  • Properties — name, date, confidence score, source URL

This structure makes knowledge searchable, connectable, queryable, and — critically — explainable: every answer can be traced back to the facts and relationships that produced it.

Entity Extraction (NER)

Scanning text to find and classify real-world entities.

# Input: "Apple Inc. was founded by Steve Jobs in 1976 in Cupertino."
{
    "entities": [
        {"text": "Apple Inc.",  "type": "ORGANIZATION", "confidence": 0.98},
        {"text": "Steve Jobs",  "type": "PERSON",       "confidence": 0.99},
        {"text": "1976",        "type": "DATE",         "confidence": 0.95},
        {"text": "Cupertino",   "type": "LOCATION",     "confidence": 0.97}
    ]
}

Each entity gets a type, confidence score, and a link to its source document.

Relationship Extraction

Finding how entities connect to each other.

{
    "relationships": [
        {"subject": "Steve Jobs", "predicate": "founded",    "object": "Apple Inc.", "confidence": 0.92},
        {"subject": "Apple Inc.", "predicate": "located_in", "object": "Cupertino",  "confidence": 0.89}
    ]
}

Relationships can be extracted via rule-based methods, ML models, or LLMs (with "llm_typed" metadata).

Embeddings

Embeddings convert text into numerical vectors so that AI systems can measure semantic similarity — finding related concepts even when the exact words differ.

Semantica uses embeddings for:

  • Semantic search — retrieve by meaning, not just keywords
  • Entity resolution — match the same entity across different sources
  • Precedent search — find similar past decisions
  • GraphRAG retrieval — hybrid vector + graph traversal

GraphRAG

GraphRAG (Graph-Augmented Retrieval Augmented Generation) enhances LLM responses by grounding them in a structured knowledge graph rather than raw text chunks alone.

How it works:

  1. User submits a query
  2. Semantica retrieves relevant graph context (entities, relationships, reasoning paths)
  3. The LLM generates a response grounded in that context
  4. Every claim in the response links back to a source node in the graph

This eliminates the hallucination and traceability problems of standard RAG.

Ontology

An ontology defines the schema and rules for your knowledge — what entity types exist, which relationships are valid, and what constraints apply.

ontology = {
    "classes": ["Person", "Organization", "Location"],
    "relationships": ["works_for", "located_in", "founded_by"],
    "rules": {
        "Person":       ["must_have_name"],
        "Organization": ["must_have_name", "can_have_founding_date"]
    }
}

Semantica can auto-generate ontologies from your knowledge graph, or import existing OWL/RDF/Turtle ontologies.

Reasoning & Inference

Semantica includes multiple reasoning engines to derive new knowledge from existing facts.

Known:    Steve Jobs founded Apple Inc.
Known:    Apple Inc. is headquartered in Cupertino
Inferred: Steve Jobs has a connection to Cupertino

Supported engines: forward chaining, Rete network, deductive, abductive, and SPARQL reasoning — all producing explainable inference paths, not black-box conclusions.

Temporal Graphs

Knowledge changes over time. Temporal graphs attach valid_from / valid_until windows to nodes and edges, enabling point-in-time queries and historical analysis.

Common uses: tracking company leadership changes, policy evolution, research timelines, financial instrument histories.

Deduplication & Entity Resolution

Real-world data contains the same entity under many names — "Apple", "Apple Inc.", "Apple Computer Inc." Semantica's deduplication pipeline detects these, merges attributes, resolves conflicts, and preserves the original source provenance.

Strategies: Jaro-Winkler similarity (v1), blocking_v2, hybrid_v2, semantic_v2 (v2 — up to 7x faster).

Provenance & Auditability

Every fact in Semantica links back to:

  • The source document it came from
  • The extraction method used
  • The ontology rules applied
  • The reasoning steps that produced any inference

This is W3C PROV-O compliant lineage — suitable for regulated industries that require audit trails.

Conflict Detection

When multiple sources disagree on the same fact, Semantica flags and resolves the conflict rather than silently picking one value.

Resolution strategies: prefer most recent, prefer most reliable source, majority vote, or flag for manual review.

Next Steps