Industrial intelligence reimagined

Ontologies: A shared language for industry

Ontologies define the structure, rules, and relationships of industrial data, acting as both a grammar and dictionary for terms. This standardisation ensures consistent meaning across departments, systems, and use cases.

In industries burdened by legacy systems and fragmented data, ontologies provide a path to true interoperability. They enable scalable integration and ensure that analytics tools, AI models, and human teams all speak the same language, thereby making reasoning, reporting, and decision-making more coherent and collaborative.

Knowledge graphs: From data points to patterns

Unlike spreadsheets or static databases, knowledge graphs represent data as a web of relationships and mirroring how humans think. In mining, for instance, a knowledge graph might link drill rigs to maintenance logs, geological data to sensor feeds, or operator shifts to production outcomes.

This networked structure not only visualizes connections but also enables machine reasoning and real-time insights. It allows systems to answer complex questions like: ‘Which drill rigs are most at risk of failure based on recent anomalies?’ or ‘How does ore grade variation correlate with processing delays downstream?’.

With knowledge graphs, businesses can identify root causes, detect weak signals, and anticipate challenges long before they impact operations.

Grounded AI: Intelligence that understands the mine

Not all AI is created equal. Many black-box models deliver predictions without visibility into how they were made, leaving operators in the dark.

Grounded AI is different. Rooted in specific business processes and combined with a foundation of knowledge graphs and ontologies, it delivers insights that are explainable, traceable, and context-aware. It aligns with operational goals, and every recommendation can be traced back to the data, logic, and assumptions that informed it. In environments like mining, where safety, efficiency, and uptime are non-negotiable, this ability is essential.

From Silicon Valley to the site floor

Tech giants like Google, Meta, Amazon, IBM, and Microsoft already use knowledge graphs and ontologies to power real-time reasoning, personalised recommendations, and intelligent automation. Innovators like Rio Tinto, Shell, and Siemens have begun to implement this technology to unify data, detect risks, and enable prescriptive decision-making. And the results speak for themselves with reduced downtime, accelerated insights, greater agility, and data-driven resilience.

SourceOne® EKPS: Intelligence with context

SourceOne® EKPS is an advanced knowledge system developed by members of the mining community for heavy industry. It integrates seamlessly with legacy infrastructure, ingests complex data from any source, grounds it in your business domain, and structures it with ontologies and knowledge graphs to power high-performance AI.

Whether you are managing a mine, refinery, or manufacturing site, SourceOne transforms scattered data into a living knowledge ecosystem that evolves alongside operations.

By eliminating silos, embedding meaning into datasets, and delivering predictive, explainable insights, SourceOne enables decisions to be made with clarity, confidence, and speed. More than just analytics, it is a smarter, more adaptive way of working that unleashes the full potential of your data.

To learn more, visit Eclipse Mining Technologies’ website, or sign up to be a SourceOne Insider and be the first to know about upcoming webcasts, blogs, and events.

Read the article online at: https://www.globalminingreview.com/special-reports/31072025/industrial-intelligence-reimagined/