Unstructured text documents contain rich semantic relationships, but search engines and LLM agents struggle to navigate complex dependency graphs without structured entity modeling.
A Knowledge Graph Construction Pipeline converts raw prose, technical documentation, and tabular corpora into machine-actionable RDF Triples \langle ext{Subject}, ext{Predicate}, ext{Object} angle bound to a formal ontology (W3C OWL / SKOS). This guide details the 5 stages of the graph construction lifecycle: Document Chunking, Named Entity Recognition (NER), Relation Extraction, Entity Resolution (Deduplication), and Graph Store Projection.
+-----------------------------------------------------------------------------------------------------------------------+
| KNOWLEDGE GRAPH PIPELINE STAGES |
+-----------------------------------------------------------------------------------------------------------------------+
| Stage | Input Payload | Primary Technique | Target Output Format |
+------------------------+----------------------------------------+----------------------------+------------------------+
| 1. Document Ingestion | Raw Markdown / PDFs / HTML | Structural AST Tokenization| Clean Section Segments |
| 2. Entity Extraction | Text Segments | Few-shot LLM / GLiNER | Entity Mentions & Types|
| 3. Relation Extraction | Entities + Context | OpenIE / Schema Constrained| Directed Triples |
| 4. Entity Resolution | Raw Entity Nodes | String Distance + Cosine | Canonical URI / IRI |
| 5. Knowledge Store Sync| Validated Triples | SPARQL Updates / Jena TDB2 | Persistent Jena Graph |
+-----------------------------------------------------------------------------------------------------------------------+
+-----------------------------+
| Raw Text Context Segment |
+--------------+--------------+
|
+--------------------+--------------------+
| |
v v
+---------------------------+ +---------------------------+
| Fast Specialized NER | | Schema-Constrained LLM |
| (GLiNER / Spacy RoBERTa) | | (Pydantic Function Call) |
| - High Speed (<15ms) | | - Rich Context Relations |
| - Fixed Entity Types | | - Disambiguates Homonyms |
+-------------+-------------+ +-------------+-------------+
| |
+--------------------+--------------------+
|
v
+-----------------------------+
| Extracted Candidate Triples |
+-----------------------------+
from pydantic import BaseModel, Field
from typing import List, Literal
class ExtractedEntity(BaseModel):
name: str = Field(description="Canonical entity name")
entity_type: Literal["Architecture", "Protocol", "Algorithm", "Framework", "Organization"]
description: str = Field(description="One-sentence technical definition")
class ExtractedRelation(BaseModel):
subject_entity: str
predicate: Literal["implements", "extends", "depends_on", "mitigates", "replaces"]
object_entity: str
evidence: str = Field(description="Verbatim sentence proving the relation")
class GraphExtractionPayload(BaseModel):
entities: List[ExtractedEntity]
relations: List[ExtractedRelation]
Raw text frequently refers to the same underlying entity using varying lexical forms: "PostgreSQL", "Postgres", "pg", and "Postgres DB". Inserting all 4 as distinct nodes fractures the Knowledge Graph.
Postgres -> PostgreSQL).owl:sameAs Projection: For cross-ontology mapping, link entities via canonical IRIs (https://wiki.wikantik.com/id/page/01KQ0P...).