Learning Concept Embeddings with a Transferable Deep Neural Reasoner
Deep Learning for and with Knowledge Graphs Track
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26m
We present a novel approach for learning embeddings of concepts from knowledge bases expressed in the ALC description logic. They reflect the semantics in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network for all the knowledge bases.
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