A Vocabulary for Describing Mapping Quality Assessment, Refinement and Validation

Randles, Alex; Crotti, Ademar; O’Sullivan, Declan

doi:10.1109/icsc50631.2021.00076

Cited by 1 publication

(1 citation statement)

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“…The objective of the framework is to improve the quality of these mappings, which will improve quality of the resulting dataset, while promoting mapping maintenance and reuse. The framework represents the quality information generated during the assessment process in RDF format using the Mapping Quality Vocabulary 4 [12,13]. MQV is used to represent and allow interchanging of provenance information relating to the creation, quality assessment and quality refinement of mapping specifications.…”

Section: Mqv Frameworkmentioning

confidence: 99%

Evaluating Quality Improvement Techniques Within the Linked Data Generation Process

Randles

O’Sullivan

2022

Towards a Knowledge-Aware AI

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Linked Data datasets when they are published typically have varying levels of quality. These datasets are created using mapping artefacts, which define the transformation rules from non-graph based data into graph based RDF data. Currently, quality issues are detected after the mapping artefact has been executed and the Linked Data has already been published. It is argued in this paper that addressing quality issues within the mapping artefacts will positively improve the quality of the resulting dataset that is generated. Furthermore, we suggest that an explicit quality process for mappings will improve quality, maintenance, and reuse. This paper describes the evaluation of the Mapping Quality Vocabulary (MQV) Framework, which aims to guide linked data producers in producing high quality datasets, by enabling the quality assessment and subsequent improvement of the mapping artefacts. The evaluation of the MQV framework consisted of 58 participants with varying level of background knowledge.

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Section: Mqv Frameworkmentioning

confidence: 99%