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用本体表示因果关系:一项系统的文献综述。

The Representation of Causality and Causation with Ontologies: A Systematic Literature Review.

作者信息

Sawesi Suhila, Rashrash Mohamed, Dammann Olaf

机构信息

Dept. of Health Informatics and Bioinformatics, Grand Valley State University, School of Computing, MI, USA.

Dept. of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA.

出版信息

Online J Public Health Inform. 2022 Sep 7;14(1):e4. doi: 10.5210/ojphi.v14i1.12577. eCollection 2022.

Abstract

OBJECTIVE

To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations.

METHODS

We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization.

RESULTS

The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented.

CONCLUSION

No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.

摘要

目的

探讨疾病相关的因果关系在当前本体中是如何被正式表示的,并识别其潜在局限性。

方法

我们在八个数据库(PubMed、电气和电子工程师协会(IEEE Xplore)、美国计算机协会(ACM)、Scopus、科学网数据库、Ontobee、OBO铸造厂和生物门户)上进行了系统的文献检索。我们纳入了1970年1月1日至2020年12月9日期间发表的研究,这些研究使用本体作为表示工具在医学领域正式表示因果关系的概念。进一步的纳入标准是发表在英文的同行评审期刊或会议论文集上。两位作者(SS、RM)独立评估研究质量,并使用经过修改的经过验证的提取网格和预先确定的分类进行内容分析。

结果

检索策略共检索到8501篇潜在相关论文,其中50篇符合纳入标准。50篇中只有14篇(28%)明确了因果关系的性质,只有7篇(14%)包含清晰且无循环的自然语言定义。尽管提到了几种因果关系理论,但没有一篇文章提供关于因果关系如何被正式表示的广泛接受的概念化。

结论

目前没有本体能够涵盖丰富的可用因果关系概念。这为开发因果关系的形式本体提供了机会。

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