Aspects of automatic ontology extension: adapting and regeneralizing dynamic updates
AOW '06 Proceedings of the second Australasian workshop on Advances in ontologies - Volume 72
I-Cog: a computational framework for integrated cognition of higher cognitive abilities
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Foundations of refinement operators for description logics
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Ideal downward refinement in the EL description logic
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Adaptive ALE-Tbox for extending terminological knowledge
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Concept learning for EL++ by refinement and reinforcement
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
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We focus on the problem of specialization in a Description Logics (DL) representation, specifically the ALN language. Standard approaches to learning in these representations are based on bottom-up algorithms that employ the lcs operator, which, in turn, produces overly specific (overfitting) and still redundant concept definitions. In the dual (top-down) perspective, this issue can be tackled by means of an ILP downward operator. Indeed, using a mapping from DL descriptions onto a clausal representation, we define a specialization operator computing maximal specializations of a concept description on the grounds of the available positive and negative examples.