The perception: a probabilistic model for information storage and organization in the brain
Neurocomputing: foundations of research
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A dissimilarity measure for ALC concept descriptions
Proceedings of the 2006 ACM symposium on Applied computing
Approximate Measures of Semantic Dissimilarity under Uncertainty
Uncertainty Reasoning for the Semantic Web I
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A procedure founded in instance-based learningis 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.