Automated approach for quality assessment of RDF resources
Abstract Introduction The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-05-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02182-8 |
| _version_ | 1797827486633951232 |
|---|---|
| author | Shuxin Zhang Nirupama Benis Ronald Cornet |
| author_facet | Shuxin Zhang Nirupama Benis Ronald Cornet |
| author_sort | Shuxin Zhang |
| collection | DOAJ |
| description | Abstract Introduction The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of RDF resources should be established, particularly in the biomedical domain in which concepts are complex and high-quality biomedical ontologies are in high demand. However, it is unclear which quality metrics for RDF resources exist that can be automated, which is required given the multitude of RDF resources. Therefore, we aim to determine these metrics and demonstrate an automated approach to assess such metrics of RDF resources. Methods An initial set of metrics are identified through literature, standards, and existing tooling. Of these, metrics are selected that fulfil these criteria: (1) objective; (2) automatable; and (3) foundational. Selected metrics are represented in RDF and semantically aligned to existing standards. These metrics are then implemented in an open-source tool. To demonstrate the tool, eight commonly used RDF resources were assessed, including data models in the healthcare domain (HL7 RIM, HL7 FHIR, CDISC CDASH), ontologies (DCT, SIO, FOAF, ORDO), and a metadata profile (GRDDL). Results Six objective metrics are identified in 3 categories: Resolvability (1), Parsability (1), and Consistency (4), and represented in RDF. The tool demonstrates that these metrics can be automated, and application in the healthcare domain shows non-resolvable URIs (ranging from 0.3% to 97%) among all eight resources and undefined URIs in HL7 RIM, and FHIR. In the tested resources no errors were found for parsability and the other three consistency metrics for correct usage of classes and properties. Conclusion We extracted six objective and automatable metrics from literature, as the foundational quality requirements of RDF resources to maximize the potential of linked data. Automated tooling to assess resources has shown to be effective to identify quality issues that must be avoided. This approach can be expanded to incorporate more automatable metrics so as to reflect additional quality dimensions with the assessment tool implementing more metrics. |
| first_indexed | 2024-04-09T12:49:05Z |
| format | Article |
| id | doaj.art-160fb9f831754470aef40bba63a526fd |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-09T12:49:05Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-160fb9f831754470aef40bba63a526fd2023-05-14T11:19:04ZengBMCBMC Medical Informatics and Decision Making1472-69472023-05-0123S111610.1186/s12911-023-02182-8Automated approach for quality assessment of RDF resourcesShuxin Zhang0Nirupama Benis1Ronald Cornet2Department of Medical Informatics, Amsterdam UMC location University of AmsterdamDepartment of Medical Informatics, Amsterdam UMC location University of AmsterdamDepartment of Medical Informatics, Amsterdam UMC location University of AmsterdamAbstract Introduction The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of RDF resources should be established, particularly in the biomedical domain in which concepts are complex and high-quality biomedical ontologies are in high demand. However, it is unclear which quality metrics for RDF resources exist that can be automated, which is required given the multitude of RDF resources. Therefore, we aim to determine these metrics and demonstrate an automated approach to assess such metrics of RDF resources. Methods An initial set of metrics are identified through literature, standards, and existing tooling. Of these, metrics are selected that fulfil these criteria: (1) objective; (2) automatable; and (3) foundational. Selected metrics are represented in RDF and semantically aligned to existing standards. These metrics are then implemented in an open-source tool. To demonstrate the tool, eight commonly used RDF resources were assessed, including data models in the healthcare domain (HL7 RIM, HL7 FHIR, CDISC CDASH), ontologies (DCT, SIO, FOAF, ORDO), and a metadata profile (GRDDL). Results Six objective metrics are identified in 3 categories: Resolvability (1), Parsability (1), and Consistency (4), and represented in RDF. The tool demonstrates that these metrics can be automated, and application in the healthcare domain shows non-resolvable URIs (ranging from 0.3% to 97%) among all eight resources and undefined URIs in HL7 RIM, and FHIR. In the tested resources no errors were found for parsability and the other three consistency metrics for correct usage of classes and properties. Conclusion We extracted six objective and automatable metrics from literature, as the foundational quality requirements of RDF resources to maximize the potential of linked data. Automated tooling to assess resources has shown to be effective to identify quality issues that must be avoided. This approach can be expanded to incorporate more automatable metrics so as to reflect additional quality dimensions with the assessment tool implementing more metrics.https://doi.org/10.1186/s12911-023-02182-8RDFOntologiesLinked dataAutomated assessmentData qualityURI |
| spellingShingle | Shuxin Zhang Nirupama Benis Ronald Cornet Automated approach for quality assessment of RDF resources BMC Medical Informatics and Decision Making RDF Ontologies Linked data Automated assessment Data quality URI |
| title | Automated approach for quality assessment of RDF resources |
| title_full | Automated approach for quality assessment of RDF resources |
| title_fullStr | Automated approach for quality assessment of RDF resources |
| title_full_unstemmed | Automated approach for quality assessment of RDF resources |
| title_short | Automated approach for quality assessment of RDF resources |
| title_sort | automated approach for quality assessment of rdf resources |
| topic | RDF Ontologies Linked data Automated assessment Data quality URI |
| url | https://doi.org/10.1186/s12911-023-02182-8 |
| work_keys_str_mv | AT shuxinzhang automatedapproachforqualityassessmentofrdfresources AT nirupamabenis automatedapproachforqualityassessmentofrdfresources AT ronaldcornet automatedapproachforqualityassessmentofrdfresources |