BookMapp- A Tool to Explore Connections Across Texts

BookMapp is a tool that allows researchers and students to explore large corpora of books and texts semantically and non-linearly by linking ontologies and themes. We use GPT-3 language models to analyze texts and extract themes.

The problem

You have a topic that you want to delve into. You have a set of large books.

  1. You want find whether the answer is in the book!
  2. Even if you find the answer, what background information do you need? Which book has it?
  3. What relationships exist between the treatment of the topic in those books?

The Solution

BookMapp creates a Knowledge Graph of the texts and themes in the books. Pull a thread from the graph and unravel the weave.

Try this out here for free, and let us know what you think.

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Really appreciate your efforts to build this from ground up in such a short time. I think a lot of researchers and enthusiasts will find it useful.

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Thanks Prashant @prashantisonline

Just to respond to a few technical points that were raised by members of this esteemed community:

The essence of BookMapp is NOT search or query. Also, the real value comes when you have more than one book uploaded in the project.

The initial query is just to bootstrap the process. The principal value proposition is to identify similarities across the uploaded set of multiple books. Internally, we are creating an n-dimensional (where n is the number of books you uploaded) graph of connected nodes across those n dimensions. If we have, say, 5 books; we create a tensor of Rank 5, with each pair of dimensions is --roughly speaking-- a matrix of relatedness. Each of the elements of the tensor is connected using a weight or relatedness to every other node. We are doing a multi-dimensional graph traversal on each click to “More like this”.

Looking forward to more feedback, which we will surely try to answer in this forum.

BookMapp

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Whatever happened with this tool? The domain name expired?

Yes, we took the tool down due to some internal reasons. Sorry to disappoint.