Capture of latent knowledge - mat2vec in KNIME

This month in Letter to nature the researchers demonstrated the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner mining the scientific literature.
They used an algorithm named mat2vec (in python).
Can we create a workflow in KNIME that really automated this?

The code is at:

Hi @karelman, welcome back to the forum!

From what I gathered so far, this mat2vec is a more specific use case of word embedding for material sciences, right? I think there would be two options: you can get started with the deep learning extensions (here is a useful blog post about word embedding in KNIME: or, if you want to use their specialized mat2vec library, use the Python Integration in a KNIME workflow.

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Hi @ana_ved mat2vec is not a specific algorithm for material Sciences, the authors use this topic as a probe of concept.
The beautiful thing with mat2vec is that could extract knowledge and relationships for any kind of topic.
Word2vec has a different goal, different functions.

I agree with you, could be used as a Python code, but I have the filling that if we can reconstruct it with Knime nodes could be more understanding and usable for a biggest population.

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