maxadamski/reasonable-embeddings
A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with neural networks
This project helps you understand complex relationships within a knowledge base by learning meaningful 'embeddings' for concepts. You provide a knowledge base, and it generates numerical representations that capture the logical relationships between concepts. This is ideal for researchers and practitioners in fields like AI or semantic web who work with structured knowledge and need to integrate it with machine learning.
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Use this if you want to perform fast, approximate reasoning and create transferable neural models from symbolic knowledge bases, especially those described in ALC description logic.
Not ideal if you are looking for an exact, traditional logical reasoner or if your primary focus is not on integrating symbolic knowledge with neural networks.
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Last pushed
Feb 07, 2023
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