| Autor: |
Adamski, Dariusz Max, Potoniec, Jędrzej |
| Předmět: |
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| Zdroj: |
Semantic Web (1570-0844); 2024, Vol. 15 Issue 4, p1333-1365, 33p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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