# How Semantica AGI Utilizes Knowledge Graphs: Architecture and Implementation

> Discover how Semantica AGI uses knowledge graphs as its core data model for ingestion, reasoning, and visualization. Explore its architecture and implementation.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: architecture
- Published: 2026-08-23

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**Semantica AGI treats knowledge graphs as the central, graph-native data model that powers ingestion, reasoning, decision tracking, and visualization through a progressive enrichment pipeline.**

The `semantica-agi/semantica` repository implements a graph-first AI architecture where the knowledge graph serves as the single source of truth for deterministic, auditable intelligence. Unlike vector-only approaches that rely on similarity search, Semantica AGI utilizes knowledge graphs to enable explicit relationship traversal, temporal reasoning, and explainable decision-making across the entire data lifecycle.

## Graph-Native Architecture

Semantica AGI adopts a **graph-first design** that constructs the knowledge graph before any vector-store indexing. As noted in the repository's [`README.md`](https://github.com/semantica-agi/semantica/blob/main/README.md) (lines 74-75), "graph traversal finds connections embeddings miss," emphasizing the priority of structural relationships over pure similarity search. This architecture ensures that entities, relationships, and their provenance exist as first-class constructs within the system.

## The Knowledge Graph Construction Pipeline

The framework organizes data processing into a progressive pipeline that transforms raw inputs into structured, queryable knowledge:

- **Ingestion & Normalization**: Raw files, web pages, databases, and streams are processed via `semantica.ingest.*` modules
- **Chunking & Extraction**: Entity-aware text splitting preserves semantic boundaries while NER and relation extractors identify structured facts
- **Graph Construction**: The `GraphBuilder` class merges entities and stitches relationships into a unified graph
- **Ontology & Reasoning**: OWL ontologies and SHACL constraints enrich the graph, supporting Rete, Datalog, and SPARQL inference
- **Decision Intelligence**: Every AI decision is recorded as a node within `ContextGraph`, creating auditable provenance trails
- **Provenance & Auditing**: W3C PROV-O lineage attaches to each fact through `ProvenanceManager`
- **Analytics & Visualization**: Interactive exploration via `KGVisualizer` with centrality and community detection algorithms

### Ingestion and Semantic Extraction

Data enters the system through `semantica.ingest.*` modules and undergoes **entity-aware chunking** via `semantica.split.*`. The `TextSplitter` preserves entity boundaries during segmentation, ensuring that `NamedEntityRecognizer` and `RelationExtractor` (located in `semantica.semantic_extract.*`) operate on coherent semantic units with configurable confidence thresholds.

### Building the Graph with GraphBuilder

The `GraphBuilder` class in [`semantica/kg/graph_builder.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/kg/graph_builder.py) serves as the core construction engine. Calling `build()` merges extracted entities and relationships into a unified knowledge graph. When initialized with `merge_entities=True`, the builder activates an `EntityResolver` to deduplicate entities across sources and a `ConflictDetector` to identify contradictory facts. Resolved identifiers propagate to relationships through the internal `_remap_relationship_endpoints` method.

## Temporal Knowledge and Entity Resolution

Semantica AGI extends traditional knowledge graphs with temporal capabilities and robust entity resolution.

### Bi-Temporal Edge Support

The `GraphBuilder` supports **bi-temporal knowledge graphs** through `add_temporal_edge()` and `create_temporal_snapshot()`. These methods distinguish between validity time (when a fact was true in the real world) and recorded time (when the system captured the data), enabling historical reasoning and point-in-time analysis.

### Entity Resolution and Conflict Handling

When `merge_entities=True`, the system instantiates an `EntityResolver` to identify and merge duplicate entity references across ingestion batches. The `ConflictDetector` flags contradictory statements for manual or automated resolution. The `_remap_relationship_endpoints` method ensures that relationship pointers update correctly after entity deduplication, maintaining graph integrity.

## Reasoning and Decision Intelligence

The knowledge graph feeds multiple reasoning engines and decision-tracking systems without requiring LLM invocations for inference.

### Ontology-Based Inference

The `semantica.reasoning.*` modules implement multiple inference engines including **forward-chaining**, **Rete networks**, **Datalog**, and **SPARQL** endpoints. These operate on the graph structure enriched by `semantica.ontology.*` modules with OWL ontologies and SHACL constraints, providing deterministic, explainable inference capabilities.

### Decision Tracking with ContextGraph

Located in [`semantica/context.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context.py), the `ContextGraph` class wraps the knowledge graph and adds decision-recording APIs. The `record_decision()` method creates first-class decision nodes linked to the KG, while `trace_decision_chain()` and `find_similar_decisions()` enable audit trails and historical pattern matching. This integration ensures every decision remains an addressable node within the same graph used for knowledge retrieval.

## Provenance and Auditing

The `ProvenanceManager` in [`semantica/provenance.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/provenance.py) attaches **W3C PROV-O** lineage metadata to each graph fact. This creates immutable audit trails that track the origin, transformation, and confidence of every entity and relationship, supporting compliance requirements and forensic analysis.

## Visualization and Analytics

The `KGVisualizer` class in [`semantica/visualization/kg_visualizer.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/kg_visualizer.py) provides interactive exploration of the knowledge graph. It supports force-directed layouts, community detection, and centrality analysis, logging actions directly from the graph builder state. This enables users to visually identify clusters, bottlenecks, and connection paths that vector embeddings cannot surface.

## End-to-End Implementation Example

The following example demonstrates the complete pipeline from document ingestion to decision recording:

```python

# 1️⃣ Ingest raw documents -------------------------------------------------

from semantica.ingest import FileIngestor
docs = FileIngestor().ingest_directory("./contracts/", recursive=True)

# 2️⃣ Extract entities & relations -----------------------------------------

from semantica.split import TextSplitter
from semantica.semantic_extract import NamedEntityRecognizer, RelationExtractor

splitter = TextSplitter(method="entity_aware", chunk_size=1000)
chunks = [splitter.split(d["text"]) for d in docs]

ner = NamedEntityRecognizer(confidence_threshold=0.7)
rel_extractor = RelationExtractor(confidence_threshold=0.6)

entities = [ner.extract_entities(c) for chunk in chunks for c in chunk]
relations = [rel_extractor.extract_relations(c, entities=ents)
             for chunk in chunks for c, ents in zip(chunk, entities)]

# 3️⃣ Build the knowledge graph -------------------------------------------

from semantica.kg import GraphBuilder
builder = GraphBuilder(merge_entities=True, enable_temporal=True)

kg = builder.build({
    "entities": [e for sub in entities for e in sub],
    "relationships": [r for sub in relations for r in sub],
})

# 4️⃣ Record a decision linked to the KG ----------------------------------

from semantica.context import ContextGraph
graph = ContextGraph(advanced_analytics=True, knowledge_graph=kg)

decision_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, and existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)

# 5️⃣ Query the graph ------------------------------------------------------

similar = graph.find_similar_decisions("cloud vendor", max_results=5)
chain   = graph.trace_decision_chain(decision_id)

```

## Summary

- **Graph-First Architecture**: Semantica AGI builds the knowledge graph before vector indexing, prioritizing relationship traversal over similarity search as documented in [`README.md`](https://github.com/semantica-agi/semantica/blob/main/README.md).
- **Temporal Support**: The `GraphBuilder` class provides bi-temporal capabilities through `add_temporal_edge()` and `create_temporal_snapshot()`.
- **Entity Resolution**: Automated deduplication via `EntityResolver` and conflict detection ensure graph integrity during construction.
- **Deterministic Reasoning**: Rete, Datalog, and SPARQL engines operating on OWL ontologies provide explainable inference without LLM calls.
- **Decision Provenance**: `ContextGraph` records decisions as first-class nodes, enabling full audit trails via `trace_decision_chain()` and W3C PROV-O lineage.

## Frequently Asked Questions

### How does Semantica AGI handle entity disambiguation during knowledge graph construction?

The `GraphBuilder` class activates an `EntityResolver` when initialized with `merge_entities=True`. This component analyzes extracted entities across ingestion batches to identify duplicates, while the `ConflictDetector` flags contradictory facts for resolution. The internal `_remap_relationship_endpoints` method updates relationship pointers after deduplication to maintain referential integrity.

### What temporal capabilities does the knowledge graph support?

`GraphBuilder` supports bi-temporal knowledge graphs through methods like `add_temporal_edge()` and `create_temporal_snapshot()`. The system distinguishes between validity time (when facts were true in reality) and recorded time (when the system captured them), enabling historical queries and point-in-time analysis that pure vector stores cannot provide.

### How does Semantica AGI track decision provenance without using LLMs?

The `ContextGraph` class in [`semantica/context.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context.py) wraps the knowledge graph and provides `record_decision()`, which creates immutable decision nodes linked to relevant graph entities. The `trace_decision_chain()` method traverses these links to reconstruct audit trails, while `ProvenanceManager` attaches W3C PROV-O metadata to each fact, ensuring deterministic traceability without probabilistic model inference.

### Can the knowledge graph be queried using standard semantic web technologies?

Yes, the `semantica.reasoning.*` modules include SPARQL endpoints and support OWL ontologies with SHACL constraints. The graph infrastructure accommodates forward-chaining inference, Rete networks, and Datalog rules, allowing integration with existing semantic web toolchains and standards-based querying.