We focus on the induction and revision of terminologies from metadata. Following a Machine Learning approach, this setting can be cast as a search problem to be solved employing operators that traverse the search space expressed in a structural representation, aiming at correct concept definitions. The progressive refinement of such definitions in a terminology is driven by the available extensional knowledge (metadata). A knowledge-intensive inductive approach to this task is presented, that can deal with on the expressive Semantic Web representations based on Description Logics, which are endowed with well-founded reasoning capabilities. The core inferential mechanism, based on multilevel counterfactuals, can be used for either inducing new concept descriptions or refining existing (incorrect) ones. The soundness of the approach and its applicability are also proved and discussed1.
Knowledge-Intensive Induction of Terminologies from Metadata
ESPOSITO, Floriana;FANIZZI, Nicola;SEMERARO, Giovanni
2004-01-01
Abstract
We focus on the induction and revision of terminologies from metadata. Following a Machine Learning approach, this setting can be cast as a search problem to be solved employing operators that traverse the search space expressed in a structural representation, aiming at correct concept definitions. The progressive refinement of such definitions in a terminology is driven by the available extensional knowledge (metadata). A knowledge-intensive inductive approach to this task is presented, that can deal with on the expressive Semantic Web representations based on Description Logics, which are endowed with well-founded reasoning capabilities. The core inferential mechanism, based on multilevel counterfactuals, can be used for either inducing new concept descriptions or refining existing (incorrect) ones. The soundness of the approach and its applicability are also proved and discussed1.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
