Abstract
A procedure founded in instance-based learning is presented, for performing a form of analogical reasoning on knowledge bases expressed in a wide range of ontology languages. The procedure exploits a novel semi-distance measure for individuals, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of features represented by concept descriptions. The procedure can answer by analogy to class’membership queries on the grounds of the classification of a number of training instances (the nearest ones w.r.t. the semi-distance measure). Particularly, it may also predict assertions that are not logically entailed by the knowledge base. In the experimentation, where we compare the procedure to a logical reasoner, we show that it can be quite accurate and augment the scope of its applicability, outperforming previous prototypes that adopted other semantic measures.
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Fanizzi, N., d’Amato, C., Esposito, F. (2007). Instance-Based Query Answering with Semantic Knowledge Bases. In: Basili, R., Pazienza, M.T. (eds) AI*IA 2007: Artificial Intelligence and Human-Oriented Computing. AI*IA 2007. Lecture Notes in Computer Science(), vol 4733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74782-6_23
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DOI: https://doi.org/10.1007/978-3-540-74782-6_23
Publisher Name: Springer, Berlin, Heidelberg
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