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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Lazy Learning from Terminological Knowledge Bases
verfasst von : Claudia d’Amato, Nicola Fanizzi
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
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This work presents a method founded on instance-based learning algorithms for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to infer class membership of instances and to predict hidden assertions that are not logically entailed from the knowledge base and need to be successively validated by humans (e.g. a knowledge engineer or a domain expert). In the experimentation, we show that the method can effectively help populating an ontology with likely assertions that could not be logically derived.