Semantica Knowledge Graph Export Pipeline: RDF, OWL, and Cypher Conversion Explained

Semantica's export pipeline converts internal knowledge graph dictionaries into RDF, OWL, or Cypher formats through a normalized dispatcher that routes to format-specific exporters with shared namespace management and validation.

The semantica-agi/semantica repository implements a modular export architecture for knowledge graph serialization. This article breaks down the complete pipeline from raw Python dictionaries to standard graph formats, with precise source code references from the implementation.

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Payload Normalization: The First Pipeline Stage

Before any export occurs, Semantica normalizes the incoming knowledge graph structure. The normalize_graph_payload() function in semantica/utils/helpers.py (around line 976) standardizes vocabulary variations into a consistent shape.

Supported input vocabularies:

• entities/relationships — the canonical Semantica format
• nodes/edges — common graph theory terminology

The normalizer coerces both into { "entities": [...], "relationships": [...] }, ensuring downstream exporters receive a predictable data structure regardless of how users constructed their graphs.

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Unified Export Entry Point: export_knowledge_graph()

The central dispatcher lives in semantica/export/methods.py at line 883. This function serves as the primary API for all export operations.

from semantica.export import export_knowledge_graph

kg = {
    "entities": [{"id": "E1", "type": "Person", "name": "Alice"}],
    "relationships": [{"source": "E1", "target": "E2", "type": "knows"}],
}

# Auto-detect format from extension

export_knowledge_graph(kg, "graph.ttl")

# Explicit format selection

export_knowledge_graph(kg, "graph.cypher", format="cypher")

The dispatcher resolves the target format through either:

• File extension inference (*.ttl → turtle, *.cypher → cypher, etc.)
• Explicit format= parameter override

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Format-Specific Exporter Dispatch

Once normalized and routed, the pipeline instantiates the appropriate exporter class. The dispatch logic at lines 883–910 in methods.py handles three primary format families.

RDF/Turtle/RDF-XML/JSON-LD/N-Triples: RDFExporter

Located in semantica/export/rdf_exporter.py, the RDFExporter class provides comprehensive RDF serialization through the export_to_rdf() method (lines 74–88).

Key components:

• NamespaceManager (lines 72–82) — Collects standard RDF prefixes plus the shared Semantica base URI from provenance.manager.DEFAULT_BASE_URI
• RDFValidator.validate_rdf_syntax (lines 60–68) — Pre-serialization syntax validation
• RDFSerializer — Produces Turtle, RDF/XML, JSON-LD, or N-Triples output

from semantica.export import RDFExporter

# Direct exporter usage

RDFExporter().export_knowledge_graph(kg, "graph.ttl")

# With explicit format

RDFExporter().export(kg, "graph.jsonld", format="jsonld")

OWL/XML and OWL/Turtle: OWLExporter

The semantica/export/owl_exporter.py module handles OWL ontology export with two internal pathways:

• _export_owl_xml() — OWL-XML serialization
• _export_owl_turtle() — OWL in Turtle syntax

from semantica.export import OWLExporter

ontology = {
    "name": "FamilyOntology",
    "classes": [{"id": "Person", "label": "Person"}],
    "object_properties": [{"id": "hasChild", "domain": "Person", "range": "Person"}],
}

OWLExporter(ontology_uri="http://example.org/family#").export(
    ontology, "family.owl", format="owl-xml"
)

Cypher/Neo4j: LPGExporter

The Labeled Property Graph exporter in semantica/export/lpg_exporter.py generates Cypher CREATE statements for Neo4j compatibility. The export_lpg() function (called from the dispatcher) routes to this class.

Core generation methods:

• _generate_node_queries() (lines 42–58) — Creates CREATE (n:Label {properties}) statements
• _generate_relationship_queries() — Creates CREATE (a)-[r:TYPE]->(b) statements

from semantica.export import LPGExporter, export_knowledge_graph

kg = {
    "entities": [{"id": "n1", "type": "Person", "name": "Bob"}],
    "relationships": [{"source": "n1", "target": "n2", "type": "knows"}],
}

# Via dispatcher

export_knowledge_graph(kg, "graph.cypher", format="cypher")

# Direct usage

LPGExporter().export(kg, "graph.cypher")

---

Namespace Management and IRI Resolution

RDF and OWL exports rely on consistent IRI generation. The RDFExporter initializes a NamespaceManager that binds:

• Standard W3C prefixes (rdf, rdfs, owl, skos)
• The Semantica base URI from DEFAULT_BASE_URI in semantica/provenance/manager.py

This ensures that exports from different sessions or components resolve to the same identifiers, critical for federated query scenarios.

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Validation and Error Handling

Each exporter performs pre-serialization validation:

Exporter	Validation Mechanism	Behavior
RDFExporter	RDFValidator.validate_rdf_syntax()	Logs warnings, continues unless fatal
OWLExporter	Lightweight structural checks	Logs and filters invalid axioms
LPGExporter	ID uniqueness and type checks	Raises on malformed node/rels

Validation errors are logged through the shared logging infrastructure without aborting the export unless explicitly configured.

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File Output and Progress Tracking

The final pipeline stage handles physical file writing. All exporters use:

• ensure_directory() — Creates parent directories as needed
• ProgressTracker — Reports serialization progress for large graphs

From RDFExporter.export() (lines 31–41) and LPGExporter.export() (lines 11–17), the generated string or Cypher script is atomically written to the target path.

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Complete Pipeline Summary

raw KG dict (entities/relationships or nodes/edges)
           │
           ▼
normalize_graph_payload() ──► standard {entities, relationships}
           │
           ▼
export_knowledge_graph() in methods.py ──► format detection
           │
    ┌──────┼──────┐
    ▼      ▼      ▼
RDFExporter  OWLExporter  LPGExporter
    │           │           │
    ▼           ▼           ▼
RDFSerializer  OWL-XML/    Cypher CREATE
(Turtle/RDF    Turtle      statements
 /JSON-LD/                 (lines 42–58)
 N-Triples)
    │           │           │
    └───────────┴───────────┘
                ▼
         file write with
         ProgressTracker

---

Summary

• Normalization occurs first — normalize_graph_payload() in helpers.py standardizes vocabulary variants
• Single entry point — export_knowledge_graph() in methods.py dispatches to format-specific exporters
• Three exporter classes — RDFExporter, OWLExporter, and LPGExporter handle their respective formats
• Shared infrastructure — Namespace management, validation, logging, and progress tracking are consistent across all exporters
• Line-precise locations — Key functions at lines 883–910 (methods.py), 72–82 and 74–88 (rdf_exporter.py), 42–58 (lpg_exporter.py)

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Frequently Asked Questions

How does Semantica detect which export format to use?

Semantica uses extension-based detection with optional override. The export_knowledge_graph() function inspects the file extension (*.ttl, *.cypher, *.owl) and maps it to internal format identifiers. Users can force a specific format with the format= parameter, which takes precedence over extension detection.

Can I use the exporter classes directly without the dispatcher?

Yes. While export_knowledge_graph() provides convenience, each exporter class (RDFExporter, OWLExporter, LPGExporter) exposes direct methods like export() and export_knowledge_graph(). Direct instantiation allows fine-grained control over namespace URIs, validation strictness, and serialization options.

What Cypher dialect does the LPGExporter produce?

The LPGExporter generates standard openCypher CREATE statements compatible with Neo4j and Apache AGE. The _generate_node_queries() and _generate_relationship_queries() methods produce property-bearing nodes and typed relationships without Neo4j-specific extensions, ensuring broad compatibility with labeled property graph databases.

Is there support for streaming or partial exports of large knowledge graphs?

The source analysis shows ProgressTracker integration for monitoring serialization progress, but does not indicate native streaming or chunked output. For very large graphs, users would need to partition the input dictionary before calling export_knowledge_graph(), as the current implementation normalizes and processes the complete payload in memory before file writing.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}