矿产勘查知识图谱

——研究进展、关键问题与未来展望

打开文本图片集

Abstract： As a complex task that heavily relies on causal reasoning and expert knowledge， mineral exploration urgently requires a computable framework for knowledge representation and reasoning centered on metalogenic processes. With the widespread applicationofbig data and artificial intelligence in geosciences，knowledge graphs have gradually become an important tool for integrating multisource geological data，explicitly representing metallogenic processes andsupporting the prediction of mineral exploration targets.This study systematically reviews the research progress of knowledge graphs inthe context of mineral exploration，focusing on ontological modeling， semantic integration，and reasoning mechanisms.Italso highlights theapplicationpathsandchallngesof graph neural networks in graph-structured representation and mineral target prediction.Although knowledge graphs demonstratesignificant advantages in modeling causal chains and enabling explainable predictions，there remain critical bottlenecks inunifiedontological standards，cross-modal data fusion，modelinterpretabilityand enginering deployment.Furthermore，current research stilldepends heavilyon expert knowledge，lackingrobustcros-modal fusion frameworksand standardized ontologies，which limit their practical applications in mineral prospecting. Looking forward，the deep integrationof knowledge，data，andmodelsisrecommended，whichshould enable researchers to explore the colaborative reasoning ofsymbolic logic， neural networks，and large language models，and to develop general-purpose knowledge graphs and service-oriented platforms for mineral exploration.

Key words： mineral exploration；knowledge graph； graph neural networks； ontological modeling； knowledgereasoning

地球科学作为典型的数据密集型学科，广泛涉及地质、地理、资源与环境等多个子领域。（剩余30903字）