| Citation: | Shaochun Dong, Yukun Shi, Yizao Ran, Haijun Wu, Yiying Deng, Junxuan Fan, Xinyu Dai. Biological Classification System Knowledge Graph and Semi-automatic Construction of Its Invertebrate Fossil Branches. Journal of Earth Science, 2024, 35(6): 2119-2128. doi: 10.1007/s12583-023-1941-y
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Biological classification is the foundation of biology and paleontology, as it arranges all the organisms in a hierarchy that humans can easily follow and understand. It is further used to reconstruct the evolution of life. A biological classification system (BCS) that includes all the established fossil taxa would be both useful and challenging for uncovering the life history. Since fossil taxa were originally recorded in various published books and articles written by natural languages, the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system. A Knowledge Graph (KG) is a formalized description framework of semantic knowledge, which represents and retrieves knowledge in a machine-understandable way, and therefore provides an eligible method to represent the BCS. In this paper, a model of the BCS KG including the ontology and fact layers is presented. To put it into practice, the ontology layer of the invertebrate fossil branches was manually developed, while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing technology. As a result, 27 348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency, and the invertebrate fossil branches of the BCS KG was thus installed. This study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.
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