ArticleBMC medical informatics and decision making2023
Automated approach for quality assessment of RDF resources.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Cross-Standard Health Data Harmonization using Semantics of Data Elements.Scientific data · 2024Article
- Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases.Journal of biomedical semantics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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.
methodsAn 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).
resultsSix 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.
conclusionWe 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.