What do Equinor and Aker BP, two of the biggest Norwegian companies in the energy sector know that others don't?

Ontologies provide a structured, machine-readable representation of knowledge that enables these companies to integrate vast amounts of disparate data across drilling operations, production facilities and maintenance systems.

Ontology-based data access accelerates decision-making and lowers costs by streamlining real-time data integration and eliminating redundancy. It standardizes global operations with a common vocabulary while its flexible framework ensures future-proofing, easily adapting to renewable energy transitions without requiring system overhauls.

But Wait…What Actually Is Ontology?

Imagine you are trying to teach a machine to understand the world. To do that and create true intelligence, you need to organize your data through three critical layers.

Ontology is a structured way of organizing knowledge. It defines the different types of things or concepts in a particular area, how they relate to each other, and the rules for these relationships.

Think of it as a vocabulary or dictionary that helps people and computers understand the meaning of information. It answers three fundamental questions:

1. What things exist? (e.g., pumps, valves, pressure sensors)
2. How should we classify them? (e.g., equipment types, failure modes)
3. How do they relate to each other? (e.g., this sensor monitors that pump)

Ontology provides computers with a rule book for understanding the world. It teaches machines what things are, how they're related, and what rules apply to them.Without an ontology, data is just a jar filled with random words and numbers.

Here's the classic problem. You have three different databases, calling the same object with different names; a computer will think they are totally different things.

• Database A calls something a "sofa"
• Database B calls it a "lounge"
• Database C calls it a "couch"

Your data scientist? She just spent three weeks trying to figure out why the furniture inventory doesn't match.

Ontology fixes this by creating semantic interoperability by saying: "In this system, 'sofa,' 'lounge,' and 'couch' mean the exact same thing."

It provides the inference rules that machines need to reason. This way machines can understand that words have meaning, things have relationships, and context matters.

Practical Example: Imagine you ask a digital assistant, "Find Italian restaurants near me." Sounds simple, right? But here's what ontology makes possible behind the scenes.

Ontology provides the framework:

• Restaurants are categorized by cuisine.
• "Italian" is a specific cuisine type.
• "Near" refers to location context.

The machine understands your request and responds with a list of nearby Italian restaurants, complete with ratings and directions.

In an oil & gas context:

An engineer asks, "Show me all pressure sensors that exceeded the threshold in the North Sea last week."

Without ontology? That query might take IT three days to fulfill.

With ontology? Instant results. The system understands sensors, thresholds, geographic locations, and time contexts seamlessy.

The machine doesn't just process your request, it understands it.

The Power Trio: Ontology, Knowledge Graphs & Context Graphs

Now here's where it gets really interesting.

What are Knowledge and Context Graphs?

Knowledge graphs and context graphs are tools that use ontologies to organize and represent information visually. These graphs are the collection of real data points connected together according to the rules of Ontology

The Knowledge Graph answers the question, "Who is connected to whom, and how?", they map out relationships between entities based on ontological rules.

Context Graph, on the other hand, adds the surrounding circumstances, situations and relationships that influence how information is used or interpreted. It's the story behind every decision, preserving the full reasoning chain. Essentially, they tell the story behind every decision, capturing the "why" and "when."

How do they work together?

Ontology provides the foundation upon which both knowledge and context graphs rely on. It defines what concepts exist and how they relate, allowing the graphs to be built in a meaningful way.

The Knowledge Graphs use ontology to visually represent relationships between concepts, making complex information easier to understand and navigate.

Context Graphs focus on the context of information and its relevance and how those specific situations and relationships influence the way information is used or interpreted.

Why does this matter?

Because context changes meaning. Incorporating ontology into data management strategies is essential for fostering intelligent data relationships, bridging the gap between data and actionable insights.

The future of AI isn't just about processing data; it's about understanding the relationships that define quality data.

Want to learn more about building intelligent data relationships? Let's talk about how ontology can transform your operations.