基于本体的三稀矿产知识图谱构建

Construction of knowledge graph of the three types of rare mineral based on ontology

长期以来,我国地质工作者开展了大量的三稀矿产调查研究工作,积累了海量的地质勘查资料,其中蕴含了极为丰富的三稀矿产矿床地质特征、矿床成因、矿床地质环境、构造背景等相关知识。如何集中管理这些数据,并且使用一种形式化的方式进行表达以支持地学知识的计算和推理,成为当前地学人工智能领域的热点问题。本文引入知识工程中知识抽取、知识表示技术,基于三稀矿产有关的非结构化数据,开展知识图谱构建。首先,明确了三稀矿产的概念、实体、关系,建立了三稀矿产的知识体系,并以此建立三稀矿产本体;其次,根据建立的三稀矿产本体使用深度学习的方式抽取知识,建立三稀矿产知识图谱;最后,为了直观表达实体之间的关系,以及矿床本体的属性集合,使用Neo4j图数据库存储三元组,将知识图谱可视化。本文的研究成果可提供三稀矿产科研、管理、三稀矿产知识获取、三稀矿产知识分析、三稀矿产知识可视化等功能,为三稀矿产领域知识图谱构建提供一个应用范例,也为后续基于知识图谱的地学知识推理、识别和管理矿产资源、成矿预测等应用提供支持。

For a long time,geological workers in our country have conducted extensive investigations and research on the three types of rare mineral,accumulating a vast amount of geological exploration data.These data contain rich knowledge about the geological characteristics,genesis,geological environment,and tectonic background of mineral deposits.However,the central management of these data and its formalized expression in a way that supports computational reasoning in geosciences has become a hot topic in the field of geoscience artificial intelligence.This paper introduces knowledge extraction and knowledge representation techniques from knowledge engineering to build a knowledge graph based on unstructured data related to the three types of rare mineral. Firstly, the concept, entities, and relationships of the three types of rare mineral are clarified, and a knowledge system of the three types of rare mineral is established, forming the ontology of the three types of rare mineral. Secondly, knowledge is extracted using deep learning based on the established ontology of the three types of rare mineral, and a knowledge graph of the three types of rare mineral is constructed. Lastly, Neo4j graph database is used to store triplets and visualize the relationships between entities and the attribute sets of the mineral deposit ontology. The research results of this paper can provide functions such as scientific research, management, knowledge acquisition, analysis, and visualization of the three types of rare mineral, serving as an application example for the construction of a knowledge graph in the field of the three types of rare mineral. It also provides support for subsequent applications in geoscience knowledge reasoning, identification and management of mineral resources, and ore deposit prediction based on knowledge graph.