From: Automated approach for quality assessment of RDF resources
SWIQA (2011) | Sieve (2012) | RDFUnit (2014) | Luzzu (2016) | Our approach (2022) | |
---|---|---|---|---|---|
Purpose | Proposal of a framework for information quality assessment of Semantic Web data | Proposal of a Linked Data Quality Assessment and Data Fusion module employed by the Linked Data Integration Framework (LDIF) | Proposal of a framework based on the data quality integrity constraints (represented in SPARQL patterns) | Proposal of a semantic framework based on Dataset Quality Ontology (daQ) | Proposal of an automated approach to assess the foundational characteristics as the starting point for linked data quality assessment |
Scope of metrics | Mixed (5) | Mixed (3) | Mixed (17) | Mixed (25) | Only foundational (6) |
Example metric | Legal Value Range Rules | Completeness | TYPRO-DEP: A resource of a specific type should have a certain property. | Basic Provenance and Extended Provenance | Non-resolvable URIs |
Example or explanation | ’The property foo:population must only contain values greater than zero. ’ | “In the use case described in this paper, the task required retrieving 3 attributes (areaTotal, foundingDate, populationTotal) for all 5565 objects (Brazilian municipalities).” | ’a dbo:Person should have a dbo:birthDate’ | ’if a dataset, usually of type void:Dataset or dcat:Dataset, has the most basic provenance information; that is information related to the creator or publisher, using the dc:creator or dc:publisher properties. ’ | ’1) Check if an RDF resource is resolvable (Boolean), or 2) check if URIs in that RDF resource are resolvable, and measure the proportion of non-resolvable URIs to all unique URIs. ’ |
Link | Not found | ||||
Tooling last update | Not found | 2014 | 2022 | 2021 | 2022 |
Novelty | The first linked data quality framework | Connection with Linked Data Integration Framework (LDIF) | Use of SPARQL pattern | Add semantic layer and quality metadata to assessment framework | Focus on foundational aspects |