Knowledge Graph Construction Pipeline: Entity Extraction, Relation Resolution, and Ontology Linking

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 \text{Subject}, \text{Predicate}, \text{Object} \rangle 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.

1. Quick-Reference: The Graph Construction Lifecycle

+-----------------------------------------------------------------------------------------------------------------------+
|                                           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  |
+-----------------------------------------------------------------------------------------------------------------------+

2. Entity & Relation Extraction (LLM vs. Small Specialized Models)

@startuml
skinparam BoxPadding 10

|Stage 1: Ingestion & Tokenization|
start
:Ingest Raw Document (Markdown / PDF / HTML);
:Parse AST & Segment into Semantic Chunks;

|Stage 2: Hybrid Extraction|
fork
  :Fast Specialized NER (GLiNER / RoBERTa);
  :Extract High-Confidence Entity Spans (<15ms);
fork again
  :Schema-Constrained LLM Extraction;
  :Extract Context Relations & Disambiguate;
end fork
:Assemble Candidate Triples <Subject, Predicate, Object>;

|Stage 3: Entity Resolution & IRI Grounding|
:Lookup Known Alias & Redirect Table;
if (Exact match found?) then (yes)
  :Assign Existing Canonical IRI;
else (no)
  :Compute Levenshtein & Embedding Cosine Sim;
  if (Similarity >= Threshold?) then (yes)
    :Merge Entity & Link via owl:sameAs;
  else (no)
    :Mint New Canonical IRI;
  endif
endif

|Stage 4: Triple Store Projection|
:Construct Validated W3C RDF Triples [[/wiki/LinkedDataAndTripleStores]];
:Execute Atomic SPARQL Update Transaction;
:Commit to Jena TDB2 Triple Store;

stop
@enduml

Structured Extraction Schema (Pydantic / OpenAI Function Calling)

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]

3. Entity Resolution & Canonical IRI Grounding

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.

Resolution Protocol

  1. Exact & Alias Table Lookup: Fast O(1) mapping against known redirects (Postgres -> PostgreSQL).
  2. Jaro-Winkler & Levenshtein Distance: Flags slight spelling variations.
  3. Embedding Cosine Clustering: For ambiguous abbreviations, compare the candidate mention vector with existing entity description embeddings.
  4. W3C owl:sameAs Projection: For cross-ontology mapping, link entities via canonical IRIs (https://wiki.wikantik.com/id/page/01KQ0P...).

References

  1. Hogan, A., et al. (2021). Knowledge Graphs. ACM Computing Surveys (CSUR), 54(4), 1-37.
  2. Zaratiana, U., et al. (2024). GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer. NAACL 2024.
  3. Brickley, D., & Guha, R. V. (2014). RDF Schema 1.1. W3C Recommendation.

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