First of all, I'd like to say that this looks like a great project and I wish you the best of luck. I've done a bit of work on building knowledge graphs from semi-structured data and I know that every aspect of it is challenging. Obviously there's the data pipelines, ETL, semantic matching/categorization, statistical models, etc. Just building a simple UI for presenting a large knowledge graph was more challenging than most front end work I've ever done.
Question: if the goal is to build a knowledge graph that can "explain how anything in the world is related to everything else" how do you measure progress toward that goal? And how do you measure the quality? Just having a bunch of topics and relationships is not a great metric in my opinion. Obviously this is still very early, but here's an example I found in about 30 seconds of clicking around:
Question: if the goal is to build a knowledge graph that can "explain how anything in the world is related to everything else" how do you measure progress toward that goal? And how do you measure the quality? Just having a bunch of topics and relationships is not a great metric in my opinion. Obviously this is still very early, but here's an example I found in about 30 seconds of clicking around:
"Evidence suggests that Heart Failure is related to Income and COVID-19." [https://www.system.com/view/topic/P0XELnR0PaK]
There are topics in System for "Obesity" and "Smoking", but those are not associated to Heart Failure.