Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases
Abstract Introduction Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to fa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Journal of Biomedical Semantics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13326-023-00299-3 |
| _version_ | 1797397711034515456 |
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| author | Shuxin Zhang Nirupama Benis Ronald Cornet |
| author_facet | Shuxin Zhang Nirupama Benis Ronald Cornet |
| author_sort | Shuxin Zhang |
| collection | DOAJ |
| description | Abstract Introduction Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources. |
| first_indexed | 2024-03-09T01:14:08Z |
| format | Article |
| id | doaj.art-e27385b5b6d0483d8461a21b34e1da74 |
| institution | Directory Open Access Journal |
| issn | 2041-1480 |
| language | English |
| last_indexed | 2024-03-09T01:14:08Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Semantics |
| spelling | doaj.art-e27385b5b6d0483d8461a21b34e1da742023-12-10T12:36:34ZengBMCJournal of Biomedical Semantics2041-14802023-12-0114111410.1186/s13326-023-00299-3Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseasesShuxin 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 Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.https://doi.org/10.1186/s13326-023-00299-3Rare diseaseQuality assessmentLinked dataRDF |
| spellingShingle | Shuxin Zhang Nirupama Benis Ronald Cornet Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases Journal of Biomedical Semantics Rare disease Quality assessment Linked data RDF |
| title | Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases |
| title_full | Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases |
| title_fullStr | Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases |
| title_full_unstemmed | Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases |
| title_short | Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases |
| title_sort | assessing resolvability parsability and consistency of rdf resources a use case in rare diseases |
| topic | Rare disease Quality assessment Linked data RDF |
| url | https://doi.org/10.1186/s13326-023-00299-3 |
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