Construction of Mineral Resources Knowledge Graph: A Case Study of Linyi City, Shandong Province, China

The efficient exploration and development of mineral resources rely on deep mining and correlation analysis of massive, multi-source, and unstructured geological data. Knowledge graph technology provides a structured solution for integrating fragmented knowledge in the field of mineral resources. This study takes the iron ore resources in Linyi City, Shandong Province as a typical case and proposes a method framework for automatically constructing a regional mineral resource knowledge graph from unstructured text. Firstly, seven types of mineral entities (location, ore body, scale, type, attitude, alteration, development degree) and five semantic relationships (type, scale, location, inclusion, development) were defined, and a high-quality Chinese annotation corpus containing 10,434 entities and 6660 relationships was constructed through domain ontology design. Secondly, BiLSTM-CRF, BiGRU-CRF, and various BERT based models were compared in the named entity recognition task, and it was found that the optimized BERT-CRF model achieved the best performance (F1 score: 82.8%). The BERT based model significantly outperforms traditional PCNN and BiGRU models, achieving an F1 score of 98.14%, which was found in relation extraction tasks. Finally, based on the extracted triples, a visual knowledge graph of iron ore resources in Linyi City was constructed using the Neo4j graph database, in order to achieve knowledge association queries and visual navigation.