Knowledge Graph Embeddings Enhance Big Data Quality Assessment
A new arXiv preprint (2605.18833) proposes a knowledge-based approach for automated big data quality assessment using knowledge graph embeddings. The method predicts missing edges between an input dataset's context representation and relevant quality rules within a knowledge graph that encodes contextual data characteristics and assessment operations. By integrating diverse representations from literature, the system generates context-specific data quality plans. This addresses limitations of existing context-aware assessment tools.
Key facts
- arXiv paper 2605.18833 proposes automated data quality assessment using knowledge graph embeddings.
- The approach predicts missing edges between dataset context and quality rules in a knowledge graph.
- Knowledge graph integrates contextual data characteristics and required quality operations.
- Method draws insights from a thorough literature investigation.
- Generates a comprehensive, context-specific data quality assessment plan.
- Aims to overcome challenges in accurate context-aware assessment for big data.
Entities
Institutions
- arXiv