A Framework for Assessing and Refining the Quality of R2RML mappings

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

doi:10.1145/3428757.3429089

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The framework assesses the quality of mappings using domain specific metrics. These metrics assess different quality aspects within the mapping, which include Vocabulary, Mapping and Data quality aspects [15]. These metrics and aspects have been inspired from the state of the art in Linked Data quality [1,2,5,[15][16][17].…”

Section: Quality Assessmentmentioning

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: Quality Assessmentmentioning

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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Data quality assurance practices in research data repositories—A systematic literature review. An Annual Review of Information Science and Technology (ARIST) paper

Stvilia,

Pang,

Lee

et al. 2024

Asso for Info Science & Tech

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Data quality issues can significantly hinder research reproducibility, data sharing, and reuse. At the forefront of addressing data quality issues are research data repositories (RDRs). This study conducted a systematic analysis of data quality assurance (DQA) practices in RDRs, guided by activity theory and data quality literature, resulting in conceptualizing a data quality assurance model (DQAM) for RDRs. DQAM outlines a DQA process comprising evaluation, intervention, and communication activities and categorizes 17 quality dimensions into intrinsic and product‐level data quality. It also details specific improvement actions for data products and identifies the essential roles, skills, standards, and tools for DQA in RDRs. By comparing DQAM with existing DQA models, the study highlights its potential to improve these models by adding a specific DQA activity structure. The theoretical implication of the study is a systematic conceptualization of DQA work in RDRs that is grounded in a comprehensive analysis of the literature and offers a refined conceptualization of DQA integration into broader frameworks of RDR evaluation. In practice, DQAM can inform the design and development of DQA workflows and tools. As a future research direction, the study suggests applying and evaluating DQAM across various domains to validate and refine this model further.

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Discovering Relationships Between Heterogeneous Declarative Mappings for RDF Knowledge Graph

Belmaksene,

Khouri

2023

Lecture Notes in Computer Science

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A Framework for Assessing and Refining the Quality of R2RML mappings

Cited by 4 publications

References 9 publications

Evaluating Quality Improvement Techniques Within the Linked Data Generation Process

Evaluating Quality Improvement Techniques Within the Linked Data Generation Process

Data quality assurance practices in research data repositories—A systematic literature review. An Annual Review of Information Science and Technology (ARIST) paper

Discovering Relationships Between Heterogeneous Declarative Mappings for RDF Knowledge Graph

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