Multi-Agent Framework for KG Error Detection
Leveraging Diverse Perspectives to Enhance Knowledge Graph Security
This research introduces a novel multi-agent framework that improves error detection in knowledge graphs by simulating diverse expert perspectives.
- Addresses limitations of existing methods by utilizing fine-grained subgraph information beyond fixed graph structures
- Employs multiple specialized agents to detect different types of errors through collaborative reasoning
- Provides transparent decision-making processes, improving both detection accuracy and explainability
- Enhances data security and integrity for downstream applications relying on knowledge graphs
This framework represents a significant advancement for maintaining secure, reliable knowledge bases in industrial applications where misinformation can have serious consequences.