A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-17 DOI:10.1038/s41597-025-04580-1
Tim Prudhomme, Giacomo De Colle, Austin Liebers, Alec Sculley, Peihong Karl Xie, Sydney Cohen, John Beverley
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Abstract

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

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来源本体到基本形式本体的语义映射方法。
出处本体(provo)是万维网联盟(W3C)推荐的本体,用于跨各种领域构建关于出处的数据。基本形式本体(Basic Formal Ontology, BFO)是一个顶级的本体ISO/IEC标准,用于构建各种各样的本体,如OBO Foundry本体和Common Core Ontology (CCO)。为了增强这两个本体、它们的扩展和它们组织的数据之间的互操作性,根据优先考虑语义和逻辑原则的特定标准,提出了一种映射方法和一组对齐。通过与provo实例的规范示例检查它们的逻辑一致性,以及查询不满足SPARQL中形式化的对齐标准的术语,来评估本体对齐。各种语义web技术被用于支持FAIR(可查找、可访问、可互操作、可重用)原则。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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