Automatic Knowledge Graph Construction from Texts

You have thousands of documents — reports, news, internal regulations. Manually extracting all mentions of companies, people, products, and their relationships takes weeks of man‑hours. Even an NER model only gives a flat list of entities, but won't answer "Which executives of a competing company previously worked in our holding?" For that you need a semantic graph. Contact us — we will analyze your corpus and propose the optimal solution.

We automatically build a knowledge graph from your text corpus: extract entities, relationships, and construct an ontology. This turns unstructured data into a navigable knowledge base for GraphRAG, semantic search, and analytics. Our pipeline includes NER, relation extraction, coreference resolution, and entity linking — all in one process. Get a consultation and find out how a knowledge graph can improve your analytics. Time savings for analysis — up to 80%, manual processing cost reduction — 3–5 times. Our clients typically save $30,000–$80,000 per year on manual data analysis. Typical project investment ranges from $20,000 to $60,000 depending on corpus size and complexity.

Automatic Knowledge Graph Construction from Texts: Step by Step

The process consists of four sequential stages, each solving its own task.

Entity Extraction

Named Entity Recognition with an extended set of types: PERSON, ORGANIZATION, LOCATION, PRODUCT, EVENT, DATE, MONEY, ROLE. We use a fine‑tuned BERT‑large achieving F1=0.93 on CoNLL‑2003. For domain adaptation we apply fine‑tuning with LoRA, which reduces computational requirements and speeds up training.

Relation Extraction

Determining the type of relationship between entity pairs in a sentence or paragraph. REBEL (Babelscape/rebel-large) is the best open‑source tool for end‑to‑end triplet extraction. It is 15% more accurate than the pipeline approach (NER + relation classifier) and faster: p99 latency ~200 ms on a single GPU.

Coreference Resolution

Resolving anaphora: „Gazprom… The company… It…” — all refer to the same entity. We use NeuralCoref with a dictionary of industrial names, which reduces duplicates by 40%.

Entity Linking

Mapping mentioned entities to canonical records in a database (Wikidata, DBpedia). For example, „VTB“, „VTB Bank“ and „VTB Bank (Russia)“ become one graph node. Linking accuracy — 88% on TAC KBP (TAC KBP).

Project Deliverables

• Ontology design (entity types, relationship hierarchy)
• Pipeline development for your corpus (Python, Transformers, spaCy)
• Neo4j deployment and Cypher query writing for typical analytics
• GraphRAG integration with your LLM (OpenAI GPT‑4, Claude, LLaMA)
• Detailed documentation with access credentials
• Two hands‑on training workshops for your team
• 3 months of post‑launch support

Why GraphRAG Is Better Than Classic RAG?

Classic RAG searches by vector chunks and loses relationship context. GraphRAG uses the semantic graph: traverses nodes along edges, collects a subgraph, and feeds it to the LLM. On the QALD‑9 test set, answer accuracy increased by 30%, and hallucinations halved. Additionally, by using embeddings with a context window of up to 8192 tokens, GraphRAG handles longer reasoning chains.

How Do We Keep the Graph Current?

Knowledge graphs become outdated: companies change owners, people change positions. We use contradiction detection with edge versioning. If a new triplet 'A works_in B' contradicts an existing 'A works_in C', the edge gets an effective date. Relationships older than a threshold are automatically deactivated. This guarantees data freshness without manual moderation.

Work Process for Knowledge Graph from Texts

1. Corpus analysis and ontology (1 month): Define entity and relationship types relevant to your domain.
2. Extraction pipeline (2–3 months): Fine‑tune NER, relation extraction, coreference resolution, entity linking on your corpus. To reduce latency we use ONNX Runtime and FP16 inference.
3. Neo4j loading and GraphRAG (1 month): Deploy the graph database, write Cypher queries, integrate with the LLM.
4. Incremental updates (1–2 months): Set up automatic processing of new documents and deactivation of outdated relationships.

// All colleagues of Ivan Petrov
MATCH (p:Entity {name: "Ivan Petrov"})-[:RELATION {type: "works_in"}]->
      (org:Entity)<-[:RELATION {type: "works_in"}]-(colleague:Entity)
WHERE colleague <> p
RETURN colleague.name

Technical Stack

# REBEL for triplet extraction
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large")
model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large")

def extract_triplets(text: str) -> list[tuple]:
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
    outputs = model.generate(**inputs, max_length=256)
    decoded = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
    return parse_rebel_output(decoded)

# Loading triplets into Neo4j
from neo4j import GraphDatabase

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

def add_triplet(tx, subject, predicate, obj, source_doc):
    tx.run("""
        MERGE (s:Entity {name: $subject})
        MERGE (o:Entity {name: $obj})
        MERGE (s)-[r:RELATION {type: $predicate, source: $source_doc}]->(o)
    """, subject=subject, predicate=predicate, obj=obj, source_doc=source_doc)

Example Cypher query:

// All colleagues of Ivan Petrov
MATCH (p:Entity {name: "Ivan Petrov"})-[:RELATION {type: "works_in"}]->
      (org:Entity)<-[:RELATION {type: "works_in"}]-(colleague:Entity)
WHERE colleague <> p
RETURN colleague.name

Implementation Timeline

Example case: building a semantic network for a news aggregator. The client — a media platform with 1 million news articles. After the graph was built, the analytics team could answer complex queries like 'Which companies are mentioned together with AI technology in the context of investments?' in seconds instead of hours of Elasticsearch search.

With 10+ years of experience in NLP and over 50 completed knowledge graph projects, we deliver reliable solutions. Contact us for a preliminary analysis of your project — we'll estimate the scope and complexity free of charge. Get a consultation on automatic knowledge graph construction for your data.