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Citation |
Feng X, Zhang K, Jiang F, Mikami Y. Int. J. Inj. Control Safe. Promot. 2023; ePub(ePub): ePub. |
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Copyright |
(Copyright © 2023, Informa - Taylor and Francis Group) |
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DOI |
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PMID |
37489820 |
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Abstract |
Understanding of how injuries occur plays an effective role in accident learning and prevention. Existing frameworks focus on crucial information but ignore their causal relationships, which can lead to an incomplete understanding of the injury process. In this study, the descriptive framework of injury data (DFID) is expanded and combined with accident causation models used to elaborate on the causality of each injury factor. Subsequently, the injury process description ontology (IPD-Onto) based on DFID (extension) is established through a seven-step method developed by Stanford University. The IPD-Onto divides injury cases into five unified classes and constructs the injury process through the object properties. The ontology-based description of the injury process (with causal relationships) provides additional description and interpretation capabilities that are understandable by human experts or computers. The results of the Protégé DL query show that the ontology-based method enables the machine to interpret the injury process. Language: en |
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Keywords |
consumer product accident; data integration; injury process description; Ontology |