How Semantica AGI Utilizes Knowledge Graphs: Architecture and Implementation

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 (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 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, 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 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 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:

# 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.
• 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 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.

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