You Need Better Knowledge Graphs for Your RAG
AI Summary
Summary: Constructing a Knowledge Graph for Data Day Texas 2024
- Objective: To learn about speakers and topics at Data Day Texas 2024 due to overlapping sessions.
- Tool Used: Diffbot to extract speaker data from the conference website.
- Data Extraction:
- Extracted speaker data into a CSV file.
- Focused on the summary column for knowledge graph generation.
- Knowledge Graph Construction:
- Used Sling Chain Stiff Bot Graph Transformer.
- Modified source code to fit the project’s needs.
- Extracted entities and relationships from text data.
- Assigned confidence degrees to facts with corresponding evidence.
- Loading into Database:
- Adjusted library code to bypass the need for document type data.
- Loaded graph document into Neo4j database.
- Knowledge Graph Examples:
- Demonstrated how the knowledge graph captures interests and relationships.
- Showed the value of knowledge graphs in connecting topics and people.
- Comparison with GPT-4:
- Decided against comparing with GPT-4 due to cost concerns.
- Highlighted previous issues with GPT-4’s entity extraction accuracy.
- Graph Analytics:
- Used the knowledge graph to identify experts in graph analytics and computer science.
- Demonstrated the AI assistant’s Q&A capability with the knowledge graph.
- Importance of Fact-Checking:
- Emphasized the need for benchmark setting, fact-checking, and evaluation in AI applications.
- Noted Diffbot’s transparency and credibility in information retrieval.
- Conclusion:
- Stressed the importance of credible information for AI application development.
- Engagement:
- Invited comments and thoughts on the video content.
GitHub Repo (mentioned for code reference, but no specific link provided)