I’m working on a project where I’m creating knowledge graphs using an LLM-based graph builder. I’m trying to ensure that the graphs generated are both accurate and free of redundancies (e.g., no duplicate nodes, correct relationships). The integrity and accuracy of these knowledge graphs are critical for my application.
My main questions are:
- Validation of Accuracy: Is there any recommended approach or best practice to confirm the accuracy of these generated graphs? For instance, how can I ensure that relationships and properties are represented correctly according to the underlying data?
- Redundancy Check: What’s the best way to identify and handle duplicate nodes or edges in Neo4j? Are there specific tools or algorithms available within Neo4j, or perhaps third-party plugins, that are particularly effective for redundancy elimination?
- Automation Possibilities: Ideally, I’m looking for a way to automate this validation process. Does anyone have experience or suggestions on automating these checks to maintain quality in the generated graphs continuously?
If anyone has experience in validating knowledge graphs created through automated or LLM-based processes, I’d love to hear your insights or any suggestions for tools, methods, or best practices to keep these graphs accurate and clean.