MINING SOFTWARE
Thriving in an AI-driven business world
Eclipse Mining Technologies built the SourceOne ® Enterprise Knowledge Performance System for mining companies to leverage all their data and transform it into information that can aid all levels of decision makers in all parts of the mining value chain .
SourceOne is differentiated from the rest of the solutions on the market in its ability to work at “ every data level ”, providing context to this data through the identification of its interrelationships and characteristics , the company says . This helps transform data into actionable information and knowledge used in all domains of the mining industry .
The system underwent a major redevelopment in 2024 , with further enhancements in functionality expected in the coming months . Much of this is focused on its Knowledge Graph system , which prepares clean , AIready data , all while addressing common data integration issues and providing a strong basis for incorporating AI – both old and new .
IM spoke to the company to find out more .
SourceOne is differentiated from the rest of the solutions on the market in its ability to work at “ every data level ”, providing context to this data through the identification of its interrelationships and characteristics , Eclipse says
around it to maximise its value .
IM : What has changed with the EKPS with these adaptations in 2024 ? Eclipse : SourceOne ’ s design focuses on a central Knowledge Graph . We ’ ve continued to polish this idea to create a system that can handle complex relationships and analytics , not just interconnect data from disparate sources . This starts with storing data in an organised manner , storing not just the data itself but also understanding how the data relates to each other . This organisation forms a domain ontology , an understood ‘ structure ’ of not only what the data is but other facts that can be logically inferred from that data .
Domain ontologies are formal representations of knowledge within a specific field or industry . They define concepts , categories and relationships between entities in a structured manner . For AI implementations , domain ontologies provide a crucial element : context that helps machines understand and interpret data more effectively . There are many benefits to incorporating domain ontologies , including improved data interpretation , which allows AI systems to understand industry-specific terminology and concepts , and enhanced data integration , as ontologies provide a common language for merging data from diverse sources . And better reasoning capabilities where AI can make more accurate inferences based on domain-specific relationships .
A Knowledge Graph structure providing context to data makes it much easier for humans and computers to do further work and analysis .
Similarly , Knowledge Graphs are interconnected networks of entities , their attributes and the relationships between them . They provide a powerful way to represent and query complex , interconnected data . In the context of AI implementation , knowledge graphs offer several advantages , including contextual understanding , as they capture the intricate relationships between different data points , flexible querying , where complex ‘ questions ’ can be answered by traversing the graph , and improved machine learning , as knowledge graphs can enhance the performance of AI models by providing structured background knowledge .
With the Knowledge Graph in place , Eclipse is building more tools
IM : Why did you need to incorporate these changes ? Eclipse : Mining companies are increasingly turning to advanced technologies like ontologies and Knowledge Graphs to streamline operations , enhance decision making and drive innovation . They have already invested time and energy in documenting business goals and processes but have not integrated this with their operational , AI , or analytical systems . By including it , AI systems can have a much clearer understanding of the broader implications of their decisions and generate more relevant answers .
While traditional data management focuses on storing and organising data , advanced knowledge systems go further by adding context , relationships and domain-specific understanding to the data . Advanced knowledge systems significantly boost the capabilities of predictive analytics by identifying hidden patterns through the representation of data in a more contextual and interconnected manner , which can help uncover non-obvious relationships . They also help improve feature engineering , where domain ontologies can guide the selection and definition of relevant features for predictive models . The structured nature of Knowledge Graphs makes tracing and explaining AI predictions easier .
IM : What are the expected results from these changes ? Eclipse : The integration of generative and predictive AI , empowered by SourceOne EKPS , presents a transformative opportunity for companies across different domains . Organisations can unlock the full potential of these technologies by building robust domain ontologies , comprehensive Knowledge Graphs and AI-ready data infrastructure . The journey towards AI adoption may be complex , but the rewards – improved performance , innovation and competitive advantage – make it an essential undertaking for forward-thinking companies . As the AI landscape evolves , those investing in strong knowledge foundations will be best positioned to adapt and thrive in an increasingly AI-driven business world .
FEBRUARY 2025 | International Mining