摘要
在基于数据驱动范式的科学研究中,构建知识图谱已被证明是获取和表征知识的有效手段之一。然而,目前已构建的矿产资源预测领域知识图谱仍然存在诸多挑战和局限性,有待进一步解决和完善。首先,针对矿产预测的本体构建问题研究相对较少,尤其是该领域现有知识图谱的本体层普遍缺乏时空语义,限制了对于矿产资源时空特征的有效表示与分析。其次,现有图谱构建方法主要为面向数据层面的文本抽取,而缺乏对于复杂逻辑关系的本体层建设,以及本体层与数据层之间的有效关联。以上问题会导致构建的地学知识图谱缺乏深层次的语义信息,难以满足矿产资源预测对表达复杂地学概念和关系的需求。针对上述问题,本研究以综合信息矿产预测理论为指导,旨在构建可应用于矿产预测任务的复杂语义知识图谱。具体而言,首先通过对矿产预测的理论和方法进行解析构建初始化领域本体,然后选择成熟的地质时间本体和地理空间本体对初始本体进行本体融合和扩展,通过嵌入时空语义有效表达地矿产资源的时空特征。此外,重点关注了本体层与数据层之间的关联建设,通过建立丰富的语义关系,实现知识图谱中各个节点之间的有效连接与信息共享。实验结果表明,采用本文所提出的方法构建的图谱在知识丰度和置信度等指标上均优于其他现有方法。这一研究为矿产预测领域提供了更为深入和全面的数据资源建设的方法支撑,有助于推动该领域的进一步发展和应用。
Knowledge graph construction is an effective means of acquiring and representing knowledge in data-driven research,however,existing knowledge graphs have many problems and limitations in mineral resource prediction.Firstly,relevant studies are few while existing knowledge graphs lack spatiotemporal semantics,which limits the effective representation and analysis of the spatiotemporal characteristics of mineral resources.Secondly,existing graph construction methods emphasize text extraction at the data level,but lack ontology construction involving complex logical relationships and lack effective association between ontology and data layers.As a result,existing knowledge graphs lack in-depth and sufficient semantic information to meet the requirement of mineral resource prediction in expressing complex geoscience concepts and relationships.To address this issue,this study takes an ontology-guided approach to construct a knowledge graph suitable for mineral prediction tasks.We first construct the initial domain ontology on the basis of in-depth understanding of mineral prediction theories and methods;we then integrate the domain ontology with selected mature geological time ontology and geographical space ontology to expand the initial ontology-by embedding spatiotemporal semantics we can effectively express the spatiotemporal characteristics of mineral resources.We also pay attention to the association between ontology and data layers-by establishing rich semantic relationships we can achieve effective inter-node connection and information sharing in the knowledge graph.Experimental results show that the knowledge graph outperformed other existing graphs in terms of knowledge richness and confidence.This study provides a methodology for multi-ontology based knowledge graph construction for mineral prediction,thereby promoting further development of this field.
作者
叶育鑫
刘家文
曾婉馨
叶水盛
YE Yuxin;LIU Jiawen;ZENG Wanxin;YE Shuisheng(College of Computer Science and Technology,Jilin University,Changchun 130012,China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;Institute of Synthetic Information for Mineral Resources Prediction,Jilin University,Changchun 130026,China)
出处
《地学前缘》
EI
CAS
CSCD
北大核心
2024年第4期16-25,共10页
Earth Science Frontiers
基金
吉林省自然科学基金项目(20220101114JC)
国家自然科学基金原创探索计划项目(42050103)。
关键词
矿产资源
知识图谱
本体工程
综合信息矿产预测理论
mineral resources
knowledge graph
ontology engineering
mineral prediction theory