Unleashing the power of data ontology and knowledge graphs in mining: A deeper dive

The power of ontology hasn’t even scratched the surface of other industries. It might transform the mining industry in fundamental ways.

By Barry Henderson

The mining industry has historically encountered difficulties keeping up with the rapid technological advancements witnessed over the last few decades. In recent years, however, mining companies have faced additional challenges, ranging from the increase in demand for minerals for batteries to increasing operating costs caused by the rise in labor, fuel (power) and services costs, not to mention that ores are now deeper and more costly to reach. 

This has spurred the mining industry into seeking new and creative ways to increase productivity and lower costs. Over the past few years, the industry has turned to technology and automation to achieve these goals, leading to the introduction of innovative equipment and solutions. However, the potential of data ontology and knowledge graphs in the mining industry has yet to be fully realized. 

We now take a deeper dive into the subject, exploring the various ways in which data ontology and knowledge graphs can revolutionize the industry; from enhancing decision-making to promoting knowledge reuse, the possibilities seem endless.

The evolution of mining software systems
The mining industry has historically relied on software systems created in the ’80s, resulting in a fragmented space with niche products for each part of the value chain, such as exploration, mining and processing. However, there has been a recent trend toward integration, with many large corporations using multiple systems across the value chain.

While full integration across the value chain is still a long way off, companies like Eclipse Mining Technologies aim to provide the industry with the tools and vendor-neutral solutions to get there. 

Data ontology and knowledge graphs play a crucial role in this quest because they help recognize objects that span the entire value chain and their relationships between each other.

The impact of technology on the mining industry
Today, few ore bodies are relatively close to the surface. Most of these have already been mined. Instead, the great majority are found deeper and often of lower grade, which translates into a lower NPV and increased operating costs. This is prompting the mining sector to use technology in search of operational efficiencies across the value chain, as adding more trucks and shovels is no longer a viable solution. 

Just using bigger and faster equipment has been leading to diminishing returns. As a result, mining companies have invested in large internal tech and IT teams, recognizing the potential for efficiency, productivity and safety improvements through technology.

The mining industry has witnessed a growing use of AI in recent years, leading to advancements in automation and efficiency. Organizations are using AI to analyze large amounts of data from various sources with the goal of identifying patterns and trends that would be too difficult to detect manually. Ultimately, this can lead to more efficient and effective decision-making while also enhancing safety and productivity.

But although technologies like AI are incredibly powerful, their effectiveness is constrained by the access to pertinent data and its quality. However, their potential grows exponentially when assisted by data ontology and knowledge graphs.

Understanding data ontology,  knowledge graphs
Data ontology defines not only how data is organized in its various formats and concepts but also how that data is linked. This premium on modeling the relationships between data unlocks a bigger-picture view that can be missed when looking at data in terms of tables and files.

In general, ontologies provide a means of identification and clarification of the entities and concepts that mining professionals want to discuss and analyze. More importantly, they identify the key relationships among those concepts, enabling a deeper understanding of the data.

The knowledge graph builds on the data ontology. Once the structure and the relationships between data types are understood, and data is brought into the system, statistical models, analytics, and more can be built on top of its strong foundation. The result is a knowledge graph.

The power of data ontology,  knowledge graphs 
As data ontology and knowledge graphs effortlessly weave the threads of data together, they open the gateway to a world of possibilities. For instance, a mining company could introduce a new object type, such as a drill rig. These established relationships empower the company to enhance the efficiency of drill rig management by thoroughly comprehending its attributes, such as model, geographic location, fuel type and technical specifications. Furthermore, it enables a deeper understanding of how drill rigs interrelate with other objects within the mining ecosystem, including rig operators, drilling plans and haul trucks, among others.

This comprehensive ontology-based approach empowers mining professionals to identify the potential effects a failure can have across the entire mining operation, extending from the mine plan all the way to shipping and sales contracts and allowing for mitigating actions like revised maintenance or even assigning backup. This is just a glimpse of some of the benefits that ontologies offer:

  • Enhancement of Object Properties: As ontologies enhance object properties with richer metadata, including more information, relationships, and context, a geological block model, as an object, could be assigned additional properties like ore mined vs. projected or ore quality based on actual recovery with information from the mine. This allows the optimization of the block modeling process, given the additional information that is now being tracked.
  • Establishing Object Relationships: Ontologies provide
    the means to establish rules that uphold standards and validate data associated with objects, ensuring that data assigned to objects is accurate, consistent, and adheres to
    predefined guidelines. This minimizes the likelihood of errors or inaccuracies in the information used for decision- making wherever.
  • Better Understanding of Data: Ontologies facilitate the development of knowledge graphs that encapsulate data and its intricate interconnections across the entire mining
    operations, from pit to port. This capability empowers stakeholders to grasp the ripple effects of specific events on
    various facets, including safety, production, revenue, shipping, maintenance, and ESG considerations, among others.

Miners have historically collected terabytes upon terabytes of data and actually processed very little of it. Mining organizations are looking to change this, and data ontology and knowledge graphs will play a crucial role in effecting this change.

Multi-domain ontologies for mining
Domain ontology represents concepts belonging to a specific world realm, such as biology, agriculture or politics. Each domain ontology typically models domain-specific definitions of terms. However, the mining industry is a complex and multifaceted industry that requires the integration of data from various domains. This is where the power of multi-domain ontologies comes into play.

Consider, for instance, a mining corporation harnessing the capabilities of multi-domain ontologies. Such a tool empowers them to intricately map the interplay between equipment, personnel, geological features and environmental factors. Through this, they gain the analytical prowess to optimize drilling and blasting strategies, ensuring precise fragmentation in alignment with predefined targets and potentially understanding the influence of environmental factors, like weather patterns and air quality, on mining production and recovery.

In the mining sector, multi-domain ontologies stand as a testament to the power of structured knowledge, allowing enterprises to navigate complexity with precision and discover efficiencies amid the intricacies of their domain.

In conclusion, the potential for data ontology and knowledge graphs to transform the mining industry is profound. As the industry grapples with increasing demand, rising costs, and the need for deeper ore exploration, these innovative technologies offer a path to greater productivity and cost efficiency.

From improving decision-making processes to promoting knowledge reuse, the application of data ontology and knowledge graphs seems endless. As we delve deeper into the potential of data ontology and knowledge graphs in the mining industry, it becomes evident that their application is not just an evolution but a revolution in how mining companies operate. 

These technologies have the capacity to reshape the industry fundamentally, making it more efficient, cost-effective, and environmentally conscious. As miners continue to harness the power of data ontology and knowledge graphs, they pave the way for a brighter, more sustainable future for the mining sector.

About the author: Barry Henderson is head of Strategic Alliances at Eclipse Mining Technologies.

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