Knowledge graph construction and knowledge discovery for porphyry copper deposits

Abstract

Porphyry copper deposits (PCDs) are the most important deposit types for essential metals of copper, molybdenum, and gold. However, they are becoming increasingly harder to discover after decades of large-scale worldwide exploration. How to build a more convincing mineral system model, by accurately identifying all indispensable theoretical components from ore deposit formation processes, to guide the PCDs exploration during the modern era of diminishing returns on fixed exploration budgets, is the most urgent work. Knowledge graph (KG), by constructing a graph database to store the concepts and entities and the complex relationships between them, has robust knowledge discovery (KD) ability to obtain new knowledge and conclusions from existing data. With the continuous publication of massive research and literature, studies on PCDs have entered a big data era. Therefore, in this study, we try to use the KG and the following KD to develop a robust workflow for knowledge graph construction and discovery, which facilitates the identification of some indispensable and previously overlooked porphyry mineral system components from published articles, preparing for linking with spatial data and exploration budgets. We provide not only a detailed roadmap for KG construction and KD of PCDs, but also a typical example of the construction and application of KG in geosciences.