# How to Build a Knowledge Graph Using Semantica: The Complete 5-Step Workflow

> Learn how to build a knowledge graph with Semantica in 5 easy steps. This guide covers data ingestion, storage, embedding, reasoning, and visualization for your AI projects.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: how-to-guide
- Published: 2026-08-22

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**To build a knowledge graph using Semantica, you orchestrate data through a five-stage pipeline: ingest raw entities and relationships with `SeedManager`, persist the structure via `TripletStore`, generate vector embeddings for semantic search, run graph algorithms with `GraphReasoner`, and render interactive visualizations through `KGVisualizer`.**

The `semantica-agi/semantica` repository provides a Python-first framework that transforms unstructured or tabular data into structured **knowledge graphs**—collections of entities, relationships, and ontologies designed for advanced reasoning and AI-driven workflows. Unlike static databases, these graphs support dynamic enrichment, blending symbolic logic with vector similarity for hybrid intelligence applications.

## Core Architecture Overview

Semantica organizes knowledge graph construction into four modular layers. Each layer maps to a specific path in the codebase, ensuring clean separation between ingestion, storage, reasoning, and presentation.

- **Ingestion & Seeding** – The `semantica.seed.seed_manager.SeedManager` class in [`semantica/seed/seed_manager.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/seed/seed_manager.py) reads source formats (CSV, JSON, Parquet) and produces the initial graph dictionary containing `entities` and `relationships` lists.
- **Storage & Persistence** – The `semantica.triplet_store.triplet_store.TripletStore` module handles backend-agnostic persistence, supporting SQLite, PostgreSQL/PGVector, Pinecone, and Neo4j via a unified interface.
- **Vectorization & Search** – Located in [`semantica/vector_store/decision_embedding_pipeline.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/decision_embedding_pipeline.py), this layer generates node embeddings and enables hybrid semantic search across the graph structure.
- **Reasoning & Visualization** – The `semantica.reasoning` package (including [`graph_reasoner.py`](https://github.com/semantica-agi/semantica/blob/main/graph_reasoner.py) and [`sparql_reasoner.py`](https://github.com/semantica-agi/semantica/blob/main/sparql_reasoner.py)) executes algorithms like PageRank and community detection, while [`semantica/visualization/kg_visualizer.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/kg_visualizer.py) provides interactive network rendering.

## Step-by-Step Implementation

### Step 1: Ingest Raw Data with SeedManager

Start by initializing the `SeedManager` to parse your source files into a graph dictionary. In [`semantica/seed/seed_manager.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/seed/seed_manager.py), the `from_csv()` method accepts paths for entities and relationships, optionally validating the structure against an ontology when `validate=True`.

```python
from semantica.seed.seed_manager import SeedManager

seed = SeedManager()
kg = seed.from_csv(
    entities_path="data/entities.csv",
    relationships_path="data/relationships.csv",
    validate=True,  # optional ontology validation

)

```

The resulting `kg` object contains two top-level keys: `entities` (a list of node objects) and `relationships` (a list of edge objects), each capable of carrying arbitrary metadata payloads.

### Step 2: Persist to the Triplet Store

Once seeded, the graph must be persisted. The `TripletStore` class in [`semantica/triplet_store/triplet_store.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/triplet_store/triplet_store.py) abstracts storage backends. Instantiate it with a backend identifier and connection string, then call `save()` to commit the graph dictionary.

```python
from semantica.triplet_store.triplet_store import TripletStore

store = TripletStore(backend="sqlite", db_path="kg.db")
store.save(kg)

```

This makes the graph available to downstream components without reloading raw source files.

### Step 3: Enrich with Vector Embeddings

To enable semantic search, generate embeddings for each node. The `embed_graph()` function in [`semantica/vector_store/decision_embedding_pipeline.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/decision_embedding_pipeline.py) batches nodes through a specified embedding model and stores vectors alongside the graph.

```python
from semantica.vector_store.decision_embedding_pipeline import embed_graph

embed_graph(
    store=store,
    model="text-embedding-ada-002",
    batch_size=64,
)

```

This step is optional but recommended for applications requiring similarity-based retrieval or LLM-augmented reasoning.

### Step 4: Execute Graph Reasoning

With the graph stored and vectorized, run analytical algorithms. The `GraphReasoner` class in [`semantica/reasoning/graph_reasoner.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/graph_reasoner.py) provides methods like `page_rank()` for centrality analysis. For RDF-style inference, use [`sparql_reasoner.py`](https://github.com/semantica-agi/semantica/blob/main/sparql_reasoner.py) to execute SPARQL queries against the store.

```python
from semantica.reasoning.graph_reasoner import GraphReasoner

reasoner = GraphReasoner(store=store)
centralities = reasoner.page_rank()
print("Top 5 central nodes:", centralities[:5])

```

The reasoner can also infer new relationships based on existing patterns, continuously enriching the graph.

### Step 5: Visualize and Export

Finally, render the graph interactively. The `KGVisualizer` class in [`semantica/visualization/kg_visualizer.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/kg_visualizer.py) pulls data from the store and renders it using Plotly or NetworkX. The visualization registry in [`semantica/visualization/methods.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/methods.py) supports network views, community coloring, and centrality-scaled node sizing.

```python
from semantica.visualization.kg_visualizer import KGVisualizer

viz = KGVisualizer(store=store)
viz.visualize_network(
    node_color="centrality",
    node_size="centrality",
    community=True,
)

```

For downstream consumption, export the graph using utilities in [`semantica/mcp/tools/export.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/mcp/tools/export.py), which support Parquet, Arrow, JSON-LD, and GraphQL formats.

## End-to-End Code Example

The following runnable script combines all five stages into a single pipeline. Execute this after installing the `semantica` package to generate a fully enriched, visualized knowledge graph from CSV inputs.

```python

# 1️⃣  Initialise a SeedManager and load raw data

from semantica.seed.seed_manager import SeedManager

seed = SeedManager()
kg = seed.from_csv(
    entities_path="data/entities.csv",
    relationships_path="data/relationships.csv",
    validate=True,
)

# 2️⃣  Persist the graph (SQLite used here)

from semantica.triplet_store.triplet_store import TripletStore

store = TripletStore(backend="sqlite", db_path="kg.db")
store.save(kg)

# 3️⃣  Enrich with node embeddings (OpenAI ada-002 as an example)

from semantica.vector_store.decision_embedding_pipeline import embed_graph

embed_graph(
    store=store,
    model="text-embedding-ada-002",
    batch_size=64,
)

# 4️⃣  Run a reasoning query – e.g., PageRank centrality

from semantica.reasoning.graph_reasoner import GraphReasoner

reasoner = GraphReasoner(store=store)
centralities = reasoner.page_rank()
print("Top 5 central nodes:", centralities[:5])

# 5️⃣  Visualise the graph interactively

from semantica.visualization.kg_visualizer import KGVisualizer

viz = KGVisualizer(store=store)
viz.visualize_network(
    node_color="centrality",
    node_size="centrality",
    community=True,
)

```

Running this pipeline creates a knowledge graph from CSV files, stores it locally, produces embeddings, computes PageRank centralities, and launches an interactive visualization.

## Summary

- **Semantica** provides a modular Python framework for building knowledge graphs through five distinct stages: ingestion, persistence, vectorization, reasoning, and visualization.
- The **`SeedManager`** class in [`semantica/seed/seed_manager.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/seed/seed_manager.py) handles initial data ingestion and optional ontology validation.
- **`TripletStore`** in [`semantica/triplet_store/triplet_store.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/triplet_store/triplet_store.py) abstracts storage backends including SQLite, PostgreSQL, Pinecone, and Neo4j.
- Vector embeddings are generated via **`embed_graph()`** in [`semantica/vector_store/decision_embedding_pipeline.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/decision_embedding_pipeline.py), enabling hybrid semantic search.
- Graph analytics such as PageRank and community detection are executed by **`GraphReasoner`** in [`semantica/reasoning/graph_reasoner.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/graph_reasoner.py).
- Interactive visualizations and multi-format exports are managed by **`KGVisualizer`** and the export utilities in [`semantica/mcp/tools/export.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/mcp/tools/export.py).

## Frequently Asked Questions

### What storage backends does Semantica support for knowledge graphs?

According to the source code in [`semantica/triplet_store/triplet_store.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/triplet_store/triplet_store.py), the `TripletStore` class supports multiple backends including **SQLite**, **PostgreSQL with PGVector**, **Pinecone**, and **Neo4j**. You specify the backend via the `backend` parameter during instantiation, allowing you to switch from local development to production vector databases without changing application logic.

### How does Semantica validate ontologies during data ingestion?

The `SeedManager` class in [`semantica/seed/seed_manager.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/seed/seed_manager.py) accepts a `validate` boolean parameter in methods like `from_csv()`. When set to `True`, the manager validates incoming entities and relationships against a predefined ontology schema, ensuring only semantically consistent data enters the graph. This prevents malformed edges or missing required properties from polluting the knowledge base.

### Can I use a custom embedding model instead of OpenAI’s Ada?

Yes. The `embed_graph()` function in [`semantica/vector_store/decision_embedding_pipeline.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/decision_embedding_pipeline.py) exposes a `model` parameter that accepts arbitrary model identifiers. While examples reference OpenAI’s `text-embedding-ada-002`, you can pass any compatible model string handled by the underlying embedding provider, allowing integration with open-source models or custom fine-tuned transformers.

### What export formats are available for serialized knowledge graphs?

The export utilities located in [`semantica/mcp/tools/export.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/mcp/tools/export.py) support several industry-standard formats. You can serialize graphs to **Parquet**, **Arrow**, **JSON-LD**, or **GraphQL** representations. These formats enable downstream consumption by analytics pipelines, LLM-augmented agents, or external knowledge-base services that require structured graph data.