Hey, have you ever wondered how we can make machines understand the relationships between different pieces of information? This is where knowledge graphs come in – a way to represent knowledge as a graph, where entities are connected by relationships. But, I’ve been thinking, what if we combined this with cosine similarity, which measures how similar two things are?
I’ve been doing some research on cosine similarity graphs, and I realized that they’re not the same as knowledge graphs. Knowledge graphs are more about representing factual information, while cosine similarity graphs are about capturing semantic similarities.
I’m curious to know if anyone has explored combining these two concepts. Could we create a graph that contains both cosine similarities and factual information? And what about using large language models (LLMs) to traverse these graphs? I’ve seen some interesting results where LLMs can effectively recall information from similarity graphs.
But, I’m more interested in using LLMs to traverse combined knowledge graphs, which would allow them to retrieve information more accurately. Has anyone tried this before? What were your findings?
I think this could be a fascinating area of research, with many potential applications. For example, imagine being able to ask a machine a question, and it can retrieve the answer from a vast graph of knowledge. Or, being able to generate text that’s not only coherent but also factual and informative.
So, let’s spark a conversation about this. What do you think about combining knowledge graphs and cosine similarity? Have you worked on anything similar? I’d love to hear your thoughts and experiences.
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