🧠 Semantica
Docling is available as a native integration in Semantica, an open-source framework for building semantic layers and knowledge graphs from unstructured data.
By combining Docling's high-fidelity structural parsing with Semantica's knowledge engineering, you can transform complex documents into AI-ready, structured knowledge for GraphRAG, AI agents, and multi-agent systems.
Why Semantica + Docling?
While Docling excels at extracting structural elements (like tables and nested headers), Semantica bridges the semantic gap by converting that structure into a queryable knowledge base.
| Feature | Docling | Semantica |
|---|---|---|
| Parsing | 💎 High-fidelity layout & table extraction | Native DoclingParser integration |
| Structuring | Markdown, JSON, HTML export | Knowledge Graph & RDF Triplet construction |
| Refining | - | Entity normalization & deduplication |
| Intelligence | - | Automated ontology generation & GraphRAG |
Components
Docling Parser
The DoclingParser is a specialized module within Semantica that uses Docling's DocumentConverter to extract high-fidelity Markdown and structured tables. It serves as the entry point for turning raw documents into semantic data.
Knowledge Graph Builder
Semantica uses the output from the DoclingParser to extract entities and relations, which are then stored in a property graph (Neo4j, FalkorDB) or a triplet store (RDF).
Installation
Install Semantica with Docling support:
pip install "semantica[all]" docling
Usage: The Semantic Pipeline
The following example demonstrates the full pipeline: parsing a document with Docling, normalizing the text, and extracting semantic triplets for a Knowledge Graph.
from semantica.parse import DoclingParser
from semantica.normalize import TextNormalizer
from semantica.split import TextSplitter
from semantica.semantic_extract import TripletExtractor
# 1. Structural Parsing with Docling
# Docling handles the complex layout and table extraction
parser = DoclingParser(enable_ocr=True)
result = parser.parse("earnings_call.pdf")
# 2. Semantic Normalization
# Standardizes text (Unicode, whitespace) to improve LLM extraction accuracy
normalizer = TextNormalizer()
clean_text = normalizer.normalize(result["full_text"])
# 3. Knowledge Extraction
# Semantica extracts semantic triplets (Subject-Predicate-Object) from the parsed structure
extractor = TripletExtractor()
triplets = extractor.extract_triplets(clean_text)
for triplet in triplets[:3]:
print(f"Extracted: {triplet.subject} --({triplet.predicate})--> {triplet.object}")
Real-World Finance Use Case
For a complete end-to-end example showing how to build a Knowledge Graph from Finance Earnings Calls using Docling and Semantica, see the Earnings Call Analysis notebook.
Transform chaotic data into intelligent knowledge with Semantica and Docling.