A practical approach to building a calcareous nannofossil knowledge graph

Abstract Following sustained development, numerous palaeontology databases and datasets of various types have been created. However, the lack of a unified standard language to describe knowledge and unclear sharing mechanisms between different databases and datasets has limited the large‐scale integration and application of paleontological data. The knowledge graph, as a key technology for semantic translation and data fusion, offers a possible solution to these challenges. Given the potential of knowledge graphs to overcome these obstacles, this paper presents a practical approach to express paleontological knowledge in a knowledge graph via the resource description framework language. By delving into the structured data associated with calcareous nannofossil biozones (the UC zone, CC zone and NC zone), we propose an ontology to describe the semantic units and logical relationships of paleontological biozones and species and then integrate relevant species records from unstructured research reports to construct a knowledge graph for calcareous nannofossils, that integrates multisource paleobiological data and knowledge reconstruction. Our focus lies in detailing the technical aspects of constructing a paleontological knowledge graph. The results demonstrate that knowledge graphs can integrate semistructured and unstructured paleontological data from various sources. This work aims to assist palaeontologists in building and utilizing knowledge graphs, serving as an initial effort for future paleontological knowledge reasoning.

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Publication Details

Journal
Geoscience Data Journal
Published
2024-10-09
DOI
https://doi.org/10.1002/gdj3.279
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00

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article

A practical approach to building a calcareous nannofossil knowledge graph

Can Cai, Chao Ma, Shengjian Zhou, Yi Zhang et al.
Geoscience Data Journal
Biomedical Text Mining and Ontologies
article

A practical approach to building a calcareous nannofossil knowledge graph

Can Cai, Chao Ma, Shengjian Zhou, Yi Zhang, Hóngyi Zhào, Chengshan Wang, Bin Hu, Lirong Chen, Shijun Jiang, Xin Li, Longgang Ye
article en

Abstract

Abstract Following sustained development, numerous palaeontology databases and datasets of various types have been created. However, the lack of a unified standard language to describe knowledge and unclear sharing mechanisms between different databases and datasets has limited the large‐scale integration and application of paleontological data. The knowledge graph, as a key technology for semantic translation and data fusion, offers a possible solution to these challenges. Given the potential of knowledge graphs to overcome these obstacles, this paper presents a practical approach to express paleontological knowledge in a knowledge graph via the resource description framework language. By delving into the structured data associated with calcareous nannofossil biozones (the UC zone, CC zone and NC zone), we propose an ontology to describe the semantic units and logical relationships of paleontological biozones and species and then integrate relevant species records from unstructured research reports to construct a knowledge graph for calcareous nannofossils, that integrates multisource paleobiological data and knowledge reconstruction. Our focus lies in detailing the technical aspects of constructing a paleontological knowledge graph. The results demonstrate that knowledge graphs can integrate semistructured and unstructured paleontological data from various sources. This work aims to assist palaeontologists in building and utilizing knowledge graphs, serving as an initial effort for future paleontological knowledge reasoning.

Geoscience Data JournalVol. 12(1)
Hohai University (CN), Chinese Academy of Sciences (CN), Ministry of Natural Resources (CN), China University of Geosciences (Beijing) (CN), Chengdu University of Technology (CN), State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (CN), Nanjing Institute of Geology and Paleontology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 84%
Biomedical Text Mining and Ontologies
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