6
The future lies in reasoning AI, which will help the
system build itself and perform its own analysis. While
generative AI is at the point where it can generate useful
suggestions, reasoning will take this much further, planning
complex implementations and suggesting sophisticated
optimizations across mining operations. This is a natural
extension of how generative AI already works inside this
system, so it will naturally work as AI continues to evolve.
CONCLUSION
This paper has presented a comprehensive approach to
organizing mining data using a knowledge graph archi-
tecture. This knowledge graph approach addresses com-
mon data integration issues and provides a strong basis for
incorporating AI, both old and new. As AI also continues
to evolve with reasoning, this knowledge graph will show
more and more benefits.
From the flexibility of the data lake to the structure of
the ontology, the knowledge graph can adapt to the myriad
challenges presented by the mining industry and be ready
for future technology to come.
REFERENCES
[1] Dahee Jung and Yosoon Choi (2021) Systematic
Review of Machine Learning Applications in Mining:
Exploration, Exploitation, and Reclamation. Special
Issue Applications of Unmanned Aerial Vehicle and
Artificial Intelligence Technologies in Mining from
Exploration to Reclamation.
[2] Jesús Pardillo et al. (2011) Using Ontologies for the
Design of Data Warehouses. International Journal of
Database Management Systems 3.
[3] Dorit S. Hochbaum (2008), The Pseudoflow algo-
rithm: A new algorithm for the maximum flow prob-
lem. Operations Research, Volume 56, Issue 4.
[4] Vaswani, A., et al. (2017). Attention is all you
need. Advances in Neural Information Processing
Systems, 30.
[5] Warren B. Powell, Reinforcement Learning and
Stochastic Optimization: A unified framework for
sequential decisions, John Wiley and Sons, Hoboken,
2022 (1100 pages).
[6] David Silver, et al. (2016) Mastering the game of Go
with deep neural networks and tree search. Nature,
Vol 529.
[7] Roberto Noriega, Yashar Pourrahimian, Hooman
Askari-Nasab (2025) Deep Reinforcement Learning
based real-time open-pit mining truck dispatch-
ing system, Computers &Operations Research,
Volume 173.
[8] Gao, Yunfan et al. (2023) “Retrieval-Augmented
Generation for Large Language Models: A
Survey.” ArXiv abs/2312.10997.
[9] Ali Mohammadjafari, Anthony S. Maida, Raju
Gottumukkala (2024) From Natural Language to
SQL: Review of LLM-based Text-to-SQL Systems.
arXiv:2410.01066 [cs.CL] https://openai.com/index/
learning-to-reason-with-llms.
[10] Petar Jovanovic, Oscar Romero, Alkis Simitsis,
Alberto Abelló, Daria Mayorova (2014) A require-
ment-driven approach to the design and evolution of
data warehouses, Information Systems, Volume 44.
[11] Dominik Durner, Viktor Leis, and Thomas Neumann.
2023. Exploiting Cloud Object Storage for High-
Performance Analytics. Proc. VLDB Endow. 16, 11
(July 2023)
The future lies in reasoning AI, which will help the
system build itself and perform its own analysis. While
generative AI is at the point where it can generate useful
suggestions, reasoning will take this much further, planning
complex implementations and suggesting sophisticated
optimizations across mining operations. This is a natural
extension of how generative AI already works inside this
system, so it will naturally work as AI continues to evolve.
CONCLUSION
This paper has presented a comprehensive approach to
organizing mining data using a knowledge graph archi-
tecture. This knowledge graph approach addresses com-
mon data integration issues and provides a strong basis for
incorporating AI, both old and new. As AI also continues
to evolve with reasoning, this knowledge graph will show
more and more benefits.
From the flexibility of the data lake to the structure of
the ontology, the knowledge graph can adapt to the myriad
challenges presented by the mining industry and be ready
for future technology to come.
REFERENCES
[1] Dahee Jung and Yosoon Choi (2021) Systematic
Review of Machine Learning Applications in Mining:
Exploration, Exploitation, and Reclamation. Special
Issue Applications of Unmanned Aerial Vehicle and
Artificial Intelligence Technologies in Mining from
Exploration to Reclamation.
[2] Jesús Pardillo et al. (2011) Using Ontologies for the
Design of Data Warehouses. International Journal of
Database Management Systems 3.
[3] Dorit S. Hochbaum (2008), The Pseudoflow algo-
rithm: A new algorithm for the maximum flow prob-
lem. Operations Research, Volume 56, Issue 4.
[4] Vaswani, A., et al. (2017). Attention is all you
need. Advances in Neural Information Processing
Systems, 30.
[5] Warren B. Powell, Reinforcement Learning and
Stochastic Optimization: A unified framework for
sequential decisions, John Wiley and Sons, Hoboken,
2022 (1100 pages).
[6] David Silver, et al. (2016) Mastering the game of Go
with deep neural networks and tree search. Nature,
Vol 529.
[7] Roberto Noriega, Yashar Pourrahimian, Hooman
Askari-Nasab (2025) Deep Reinforcement Learning
based real-time open-pit mining truck dispatch-
ing system, Computers &Operations Research,
Volume 173.
[8] Gao, Yunfan et al. (2023) “Retrieval-Augmented
Generation for Large Language Models: A
Survey.” ArXiv abs/2312.10997.
[9] Ali Mohammadjafari, Anthony S. Maida, Raju
Gottumukkala (2024) From Natural Language to
SQL: Review of LLM-based Text-to-SQL Systems.
arXiv:2410.01066 [cs.CL] https://openai.com/index/
learning-to-reason-with-llms.
[10] Petar Jovanovic, Oscar Romero, Alkis Simitsis,
Alberto Abelló, Daria Mayorova (2014) A require-
ment-driven approach to the design and evolution of
data warehouses, Information Systems, Volume 44.
[11] Dominik Durner, Viktor Leis, and Thomas Neumann.
2023. Exploiting Cloud Object Storage for High-
Performance Analytics. Proc. VLDB Endow. 16, 11
(July 2023)