22 definitions found across 22 frameworks
| Framework | Definition | Notes |
|---|---|---|
| KGHeartBeat: a Knowledge Graph Quality
Assessment Tool 2024 |
Quality dimension from KGHeartBeat: a Knowledge Graph Quality Assessment Tool (2024) | — |
| KGHeartBeat: a Knowledge Graph Quality
Assessment Tool 2024 |
An open-source tool for periodically evaluating the quality of KGs. (2024) | — |
| Quality Without Borders: A Modular Approach to Unified
Knowledge Graph Assessment 2024 |
Quality dimension from Quality Without Borders: A Modular Approach to Unified Knowledge Graph Assessment (2024) | — |
| Quality Without Borders: A Modular Approach to Unified
Knowledge Graph Assessment 2024 |
Proposes a comprehensive Shared Framework to formally align KG-specific quality metrics, FAIR principles, and the 5-star open data scheme. (2024) | — |
| KGMM -A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration 2022 |
Quality dimension from KGMM -A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration (2022) | — |
| KGMM -A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration 2022 |
Proposes the Knowledge Graph Maturity Model (KGMM) as a structured framework for evaluating KG maturity across five levels. (2022) | — |
| Introducing the Data Quality Vocabulary (DQV) 2021 |
Quality dimension from Introducing the Data Quality Vocabulary (DQV) (2021) | — |
| Introducing the Data Quality Vocabulary (DQV) 2021 |
Defines the W3C standardized vocabulary for describing quality metadata in a machine-readable format. (2021) | — |
| A Scalable Framework for Quality Assessment of RDF Datasets. 2020 |
Quality dimension from A Scalable Framework for Quality Assessment of RDF Datasets. (2020) | — |
| A Scalable Framework for Quality Assessment of RDF Datasets. 2020 |
Presents an open source implementation of quality assessment of large RDF datasets that can scale out to a cluster of machines. (2020) | — |
| F-UJI and FAIR-Checker: Automated FAIR Data Assessment Tools 2020 |
Quality dimension from F-UJI and FAIR-Checker: Automated FAIR Data Assessment Tools (2020) | — |
| F-UJI and FAIR-Checker: Automated FAIR Data Assessment Tools 2020 |
F-UJI and FAIR-Checker are automated tools that operationalize FAIR principles for data and metadata evaluation. They test digital resources against Findable, Accessible, Interoperable, and Reusable criteria and provide compliance scoring and actionable improvement recommendations. (2020) | — |
| FAIR Data Maturity Model: specification and guidelines 2020 |
Quality dimension from FAIR Data Maturity Model: specification and guidelines (2020) | — |
| FAIR Data Maturity Model: specification and guidelines 2020 |
Establishes a common set of core assessment criteria and maturity levels for evaluating FAIRness. (2020) | — |
| ISO/IEC 25012 Data Quality Model 2014 |
Quality dimension from ISO/IEC 25012 Data Quality Model (2014) | — |
| ISO/IEC 25012 Data Quality Model 2014 |
Defines 15 data quality characteristics categorized by Inherent (data itself) and System-Dependent (data quality preserved in a computer system) viewpoints. (2014) | — |
| Linked Data – Design Issues. 2006 |
Quality dimension from Linked Data – Design Issues. (2006) | — |
| Linked Data – Design Issues. 2006 |
The document outlines the fundamental design principles behind Linked Data on the Web. It proposes a set of rules for how data should be identified, accessed, described, and linked to other resources to enable a global web of data. These ideas became the cornerstone of the Semantic Web and inspired many later frameworks for data and knowledge graph quality. (2006) | — |
| Data Quality in Context 1997 |
Quality dimension from Data Quality in Context (1997) | — |
| Data Quality in Context 1997 |
Seminal work discussing data quality across various dimensions in organizational contexts. (1997) | — |
| Beyond Accuracy: What data quality means to Data Consumers 1996 |
Quality dimension from Beyond Accuracy: What data quality means to Data Consumers (1996) | — |
| Beyond Accuracy: What data quality means to Data Consumers 1996 |
Shifted the focus of data quality discussions beyond mere accuracy to a broader contextual understanding of data quality for consumers. (1996) | — |