Reason-able Embeddings: Learning Concept Embeddings with a Transferable Neural Reasoner

Tracking #: 3355-4569

Dariusz Max Adamski
Jedrzej Potoniec

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Guest Editors NeSy 2022

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Full Paper
We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts 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, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.
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Review #1
Anonymous submitted on 14/Mar/2023
Review Comment:

I have double-checked the modifications done by the authors to address my concerns. I am satisfied with the changes. I only have one minor comment about the newly added text, which is listed below:

- page 2, line 3: "While we imagine, that" -> remove comma