emph{Fuzzy Description Logics} (fuzzy DLs) have been proposed as a mean to describe structured knowledge with vague concepts. Unlike classical DLs, were an answer to a query is a set of tuples that satisfy a query, in fuzzy DLs an answer is a set of tuples ranked according to the degree they satisfy the query. In this paper, we consider fdlliteminus. We show how to compute efficiently the top-$k$ answers of a complex query (ie~conjunctive queries) over a huge set of instances.
Answering vague Queries in fuzzy DL-Lite
Straccia U
2005
Abstract
emph{Fuzzy Description Logics} (fuzzy DLs) have been proposed as a mean to describe structured knowledge with vague concepts. Unlike classical DLs, were an answer to a query is a set of tuples that satisfy a query, in fuzzy DLs an answer is a set of tuples ranked according to the degree they satisfy the query. In this paper, we consider fdlliteminus. We show how to compute efficiently the top-$k$ answers of a complex query (ie~conjunctive queries) over a huge set of instances.File in questo prodotto:
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