44 definitions found across 44 frameworks
| Framework | Definition | Notes |
|---|---|---|
| From Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life Cycles 2025 |
Quality dimension from From Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life Cycles (2025) | — |
| From Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life Cycles 2025 |
Vision paper addressing challenges in managing KGs, proposing KGE ecosystems and life cycles to systematically manage tasks like standardization, continuous updates, and provenance tracking. (2025) | — |
| Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs (MAKGED). 2025 |
Quality dimension from Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs (MAKGED). (2025) | — |
| Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs (MAKGED). 2025 |
Introduces MAKGED, a multi-agent LLM+GCN approach for KG error detection. Four agents (head-forward/backward, tail-forward/backward) are trained on bidirectional subgraph embeddings concatenated with LLM query embeddings. They discuss and vote on each triple’s correctness. Outperforms SOTA by +0.73% on FB15k and +6.62% on WN18RR. (2025) | — |
| A Novel Customizing Knowledge Graph Evaluation Method for Incorporating User Needs. 2024 |
Quality dimension from A Novel Customizing Knowledge Graph Evaluation Method for Incorporating User Needs. (2024) | — |
| A Novel Customizing Knowledge Graph Evaluation Method for Incorporating User Needs. 2024 |
Introduces an accuracy-focused KG evaluation method that incorporates user requirements. It designs an effective two-stage weighted cluster sampling (EP-TWCS) to focus on user-important entities. Experiments show the sampled accuracy closely matches true accuracy with minimal sample size. (2024) | — |
| Assessing the Quality of a Knowledge Graph via Link Prediction Tasks (LP-Measure) 2024 |
Quality dimension from Assessing the Quality of a Knowledge Graph via Link Prediction Tasks (LP-Measure) (2024) | — |
| Assessing the Quality of a Knowledge Graph via Link Prediction Tasks (LP-Measure) 2024 |
Introduces the LP-Measure, an automated approach to assess consistency and redundancy using link prediction tasks, eliminating the need for gold standards. (2024) | — |
| Contextual knowledge graph approach to bias-reduced decision support systems 2024 |
Quality dimension from Contextual knowledge graph approach to bias-reduced decision support systems (2024) | — |
| Contextual knowledge graph approach to bias-reduced decision support systems 2024 |
Proposes a contextual KG approach to capture relationships between task/features/context, identifying bias in AI/ML model datasets. Uses debiased datasets for fairer decision-making. (2024) | — |
| Continuous Knowledge Graph Quality Assessment through Comparison using ABECTO. 2024 |
Quality dimension from Continuous Knowledge Graph Quality Assessment through Comparison using ABECTO. (2024) | — |
| Continuous Knowledge Graph Quality Assessment through Comparison using ABECTO. 2024 |
Presents ABECTO, a command-line tool for continuous QA of RDF knowledge graphs. It automatically compares multiple overlapping KGs to spot missing or incorrect values. The tool outputs quality annotations (deviations, completeness) over time, enabling CI-style monitoring. (2024) | — |
| 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) | — |
| Knowledge Graph Quality Management: A Comprehensive Survey. 2023 |
Quality dimension from Knowledge Graph Quality Management: A Comprehensive Survey. (2023) | — |
| Knowledge Graph Quality Management: A Comprehensive Survey. 2023 |
Provides a systematic survey of knowledge graph quality management, covering everything from theory to practice. It reviews quality issues, dimensions (accuracy, completeness, etc.), metrics, assessment methods, and quality improvement processes. (2023) | — |
| 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) | — |
| Linked Data Quality Assessment: A Survey 2022 |
Quality dimension from Linked Data Quality Assessment: A Survey (2022) | — |
| Linked Data Quality Assessment: A Survey 2022 |
Identifies state of the art, highlighting fragmentation, and proposing a solution based on ontology to build an end-to-end system. (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) | — |
| Knowledge Graph Quality Control: A Survey 2021 |
Quality dimension from Knowledge Graph Quality Control: A Survey (2021) | — |
| Knowledge Graph Quality Control: A Survey 2021 |
Surveys Linked Data/KG quality control. Defines six evaluation dimensions (e.g., completeness, accuracy, timeliness, trust) and analyzes their relationships. Reviews how to identify and mitigate errors during KG construction and maintenance. (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) | — |
| A Practical Framework for Evaluating the Quality of Knowledge Graphs. 2019 |
Quality dimension from A Practical Framework for Evaluating the Quality of Knowledge Graphs. (2019) | — |
| A Practical Framework for Evaluating the Quality of Knowledge Graphs. 2019 |
Surveys existing KG evaluation practices and proposes a practical quality framework. It identifies key dimensions (“fit for purpose”), selects metrics per use-case requirements, and suggests evaluation procedures (e.g., scalable sampling for metrics). (2019) | — |
| Efficient Knowledge Graph Accuracy Evaluation 2019 |
Quality dimension from Efficient Knowledge Graph Accuracy Evaluation (2019) | — |
| Efficient Knowledge Graph Accuracy Evaluation 2019 |
Proposes an efficient sampling framework to estimate large-scale KG accuracy with strong statistical guarantees while minimizing the cost of human annotation. (2019) | — |
| Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods 2017 |
Quality dimension from Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods (2017) | — |
| Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods 2017 |
Surveys KG refinement (completeness and error-correction). Reviews methods for inferring missing facts and detecting errors in KGs, along with their evaluation. Summarizes both completion and correction techniques, and categorizes evaluation methodologies. (2017) | — |
| From Data Quality to Big Data Quality: A Systematization 2015 |
Quality dimension from From Data Quality to Big Data Quality: A Systematization (2015) | — |
| From Data Quality to Big Data Quality: A Systematization 2015 |
Examines the relationship between Data Quality and Big Data characteristics (Variety, Volume), focusing on various data types (LOD, sensor data). (2015) | — |
| ISO/IEC 25024 & ISO 8000: Data Quality Measurement and Metadata Standards 2015 |
Quality dimension from ISO/IEC 25024 & ISO 8000: Data Quality Measurement and Metadata Standards (2015) | — |
| ISO/IEC 25024 & ISO 8000: Data Quality Measurement and Metadata Standards 2015 |
ISO/IEC 25024 extends ISO 25012 by defining formal methods for measuring data quality, while ISO 8000 introduces standardized data governance and master data management guidelines. (2015) | — |
| Quality assessment for Linked Data: A Survey 2015 |
Quality dimension from Quality assessment for Linked Data: A Survey (2015) | — |
| Quality assessment for Linked Data: A Survey 2015 |
Summarizes linked data quality assessment papers, introducing existing methods, and categorizing dimensions into six groups. (2015) | — |
| 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) | — |
| 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) | — |