Abstract
This work presents a method founded in instance-based learning for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to answer class membership queries and to predict new assertions that may be not logically entailed by the knowledge base. These tasks may be the baseline for other inductive methods for ontology construction and evolution. In a preliminary experimentation, we show that the method is sound and it is actually able to induce new assertions that might be acquired in the knowledge base.
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d’Amato, C., Fanizzi, N., Esposito, F. (2008). Analogical Reasoning in Description Logics. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_19
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DOI: https://doi.org/10.1007/978-3-540-89765-1_19
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