Developing an ontological framework for effective data quality assessment and knowledge modelling

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dc.contributor.author Latsou, Christina
dc.contributor.author Garcia I Minguell, Marta
dc.contributor.author Sonmez, Ayse Nur
dc.contributor.author Orteu I Irurre, Roger
dc.contributor.author Palmisano, Martin Mark
dc.contributor.author Landon-Valdez, Suresh
dc.contributor.author Erkoyuncu, John Ahmet
dc.contributor.author Addepalli, Pavan
dc.contributor.author Sibson, Jim
dc.contributor.author Silvey, Olly
dc.date.accessioned 2022-11-08T11:59:23Z
dc.date.available 2022-11-08T11:59:23Z
dc.date.issued 08/11/2022
dc.identifier.citation Latsou C, Garcia I Minguell M, Sonmez AN, et al., (2022) Developing an ontological framework for effective data quality assessment and knowledge modelling. In: 11th International Conference on Through-life Engineering Services - TESConf 2022, 8-9 November 2022, Cranfield UK, Paper number 5379 en_UK
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/18671
dc.description 11th International Conference on Through-life Engineering Services - TESConf 2022, 8-9 November 2022, Cranfield UK en_UK
dc.description.abstract Big data has become a major challenge in the 21st century, with research being carried out to classify, mine and extract knowledge from data obtained from disparate sources. Abundant data sources with non-standard structures complicate even more the arduous process of data integration. Currently, the major requirement is to understand the data available and detect data quality issues, with research being conducted to establish data quality assessment methods. Further, the focus is to improve data quality and maturity so that early onset of problems can be predicted and handled effectively. However, the literature highlights that comprehensive analysis, and research of data quality standards and assessment methods are still lacking. To handle these challenges, this paper presents a structured framework to standardise the process of assessing the quality of data and modelling the knowledge obtained from such an assessment by implementing an ontology. The main steps of the framework are: (i) identify user’s requirements; (ii) measure the quality of data considering data quality issues, dimensions and their metrics, and visualise this information into a data quality assessment (DQA) report; and (iii) capture the knowledge from the DQA report using an ontology that models the DQA insights in a standard reusable way. Following the proposed framework, an Excel-based tool to measure the quality of data and identify emerging issues is developed. An ontology, created in Protégé, provides a standard structure to model the data quality insights obtained from the assessment, while it is frequently updated to enrich captured knowledge, reducing time and costs for future projects. An industrial case study in the context of Through life Engineering Services, using operational data of high value engineering assets, is employed to validate the proposed ontological framework and tool; the results show a well-structured guide that can effectively assess data quality and model knowledge. en_UK
dc.description.sponsorship DMG Mori en_UK
dc.language.iso en en_UK
dc.publisher Cranfield University en_UK
dc.relation.ispartofseries TESConf2022;5379
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject data quality issues en_UK
dc.subject data quality dimensions en_UK
dc.subject data quality assessment en_UK
dc.subject ontology en_UK
dc.subject data management en_UK
dc.title Developing an ontological framework for effective data quality assessment and knowledge modelling en_UK
dc.type Conference paper en_UK


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