Knowledge Graphs: structuring knowledge for AI and business

In the era of generative artificial intelligence and conversational assistants like ChatGPT, access to relevant, contextualized and understandable information by an AI has become a strategic issue. This is where the Knowledge Graphs (KG) take on their full value.

But what is a Knowledge Graph, how does it differ from traditional databases (SQL or NoSQL), and how can it turn your data into a competitive advantage? This article offers you an in-depth and educational exploration of this key technology.

1. Definition: what is a Knowledge Graph?

One Knowledge Graph is a structured representation of knowledge, in which information is stored in the form of nodes (entities) and ties (relationships). It is a semantic graph that links concepts together in an explicit way, allowing a machine to interpret and reason about these relationships.

Unlike traditional databases, a Knowledge Graph doesn't just store data, he models relationships between concepts : it gives meaning to information.

Example:
“Paul purchased Product A” → “Product A belongs to the premium range” → “Product A is linked to incident #4567” → “Resolved by Documentation Z”

2. Knowledge Graph vs SQL vs NoSQL: what are the differences?

SQL: the classic relational structure

• Storage in the form of tables (rows and columns).
• Relationships defined by foreign keys (foreign keys).
• Very efficient for well-structured data and standardized use cases.
• But: often requires complex joints to rebuild business relationships.

SQL example: Customers table: Paul, ID 123
Products table: Product A, ID 456
Orders table: ID 789, Customer_ID = 123, Product_ID = 456

NoSQL: the flexibility of documents

• Representation of data in the form of documents (JSON, BSON...).
• More flexible than SQL, ideal for scalable structures.
• Relationships possible but often Implicit or stored redundantly.

NoSQL example (JSON):

JSON

CopyEdit

{
“customer”: “Paul”,
“purchases”: [
{
“product”: “Product A”,
“incident”: “4567",
“solution”: “Z documentation”
}
]
}


• Practical, but not very usable by an AI without an explicit relational structure.

Knowledge Graph: The power of connections

• Represents the relationships between entities as first-class objects.
• Each element (customer, product, documentation, incident...) is a unique entity.
• The links between them are semantically explicit.
• Perfectly usable by AI models (LLMs, recommendation systems, internal search engines...).

Visual example: [Paul] — (purchased) —> [Product A]
[Product A] — (is linked to) —> [Incident #4567]
[Incident #4567] — (has for solution) —> [Z documentation]

3. Key benefits of Knowledge Graphs

✅ Semantic comprehension

AI can navigate through data as a network of concepts, which greatly improves relevance of the answers.

✅ Reduction in “AI hallucinations”

Models like ChatGPT, when connected to a Knowledge Graph, rely on a reliable repository, which Reduces factual errors.

✅ Flexibility and scalability

Adding a new entity or relationship does not involve modifying a rigid schema: the graph adapts naturally.

✅ Intuitive exploration

The graph can be visualized, queried, and navigated visually — facilitating decision making and analysis.

✅ Interoperability

Knowledge Graphs integrate well with existing bases : they do not replace your SQL/NoSQL databases, they enrich them.

4. Concrete use cases

1. Internal AI assistant (Chatbot connected to a KG)

A chatbot can answer complex questions like:

“What incidents are frequently associated with this product for this type of customer?”

2. Smart documentation search

Instead of doing a keyword search, the user can ask a business question.
Ex: “What procedure should I follow for model X in case of an error Y?”

3. Content or action recommendation

Based on relationships in the graph: similar customers, history, behaviors...

4. Compliance and traceability audit

Monitoring dependencies between documents, regulatory obligations, suppliers...

5. The limits you need to know

• Complex initial modeling : building a good graph requires a thorough understanding of your job.
• Significant implementation cost : especially if the original data is not cleaned up.
• Need adapted tools : Neo4j, Ontotext, Stardog, RDF + SPARQL, etc.

But the long-term benefits greatly exceed these constraints, especially if the graph is connected to a LLM like GPT.

Conclusion

The Knowledge Graph is much more than a storage format. It is a How to think of data as a network of meaning, at the service of AI, decision-making and business agility.

In a world where information is abundant but often compartmentalized, companies that structure their knowledge via a graph take a head start.