Automated Big Data Quality Assessment using Knowledge Graph Embeddings
A new approach to automated data quality assessment using knowledge graph embeddings has been proposed. This method aims to enhance the accuracy of context-aware assessments by predicting missing edges in datasets. The evaluation of this approach, using a real-world dataset, demonstrates its effectiveness in generating comprehensive data quality assessment plans.
- ▪Automated data quality assessment is essential for managing big data effectively.
- ▪The proposed method integrates knowledge graph embeddings to improve context-aware assessments.
- ▪Evaluation results indicate that the approach can create detailed data quality assessment plans.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18833 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Summary source text | contentText |
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| Publisher visit | Yes — open original |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.18833 (cs) [Submitted on 12 May 2026] Title:Automated Big Data Quality Assessment using Knowledge Graph Embeddings Authors:Hadi Fadlallah, Rima Kilany, Mitri Haber, Ali Jaber View a PDF of the paper titled Automated Big Data Quality Assessment using Knowledge Graph Embeddings, by Hadi Fadlallah and 3 other authors View PDF HTML (experimental) Abstract:Automated data quality assessment is crucial for managing big data, but existing solutions face challenges in achieving accurate context-aware assessment. This paper presents a novel knowledge-based approach to enhance automated data quality assessment.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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