Crafting Knowledge Graphs: Exploring Code, Python, and Connectivity!
In this video, I dive into the exciting world of knowledge graph generation and how it can be adapted to different texts. I showcase examples from previous sessions, exploring how code and even random text can shape the resulting graph. Watch as we play with Python code and see how it transforms into something that looks like a virus (but isn't!). Along the way, I address some common challenges like nodes that aren't connected to anything, filtering out unnecessary parts of the graph, and ensuring everything works efficiently.
I'll also touch on using GitHub Copilot for coding, reflecting on the unpredictability of AI tools—sometimes they generate endless code, and other times they get lazy! Plus, I’ll share tips on keeping connections meaningful in your knowledge graphs, just like in real life.
Let me know your thoughts on this approach and how you use knowledge graphs! Make sure to check out my website https://bionichaos.com/Knowledge for more insights.
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0:00 Jumping into Knowledge Craft Generator
0:10 Overview of prompts and the importance of text in graph generation
0:29 How knowledge graphs adapt to different inputs, including code
0:47 Exploring how code in the graph looks like a virus
1:01 Reviewing disconnected nodes and filtering unnecessary parts
1:20 Trying out Python code as an example
1:41 Discussing the unpredictability of GitHub Copilot
2:00 The struggle between too much and too little code generation
2:30 Connecting the dots: Real-life connections vs graph nodes
3:00 Repeated nodes issue: Overcoming graph expansion challenges
3:32 Final clean-up of disconnected nodes
4:00 Avoiding text overflow and proximity-based issues
4:38 Wrapping up with connections between contextually related entities
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