Fuzzy quantified queries have been studied over several types of data and allow to sum up large volumes of data in a very intuitive manner. However, few works have been led on Resource Description Framework (RDF) graph databases. In this paper, we introduce the notion of fuzzy quantified queries in a (fuzzy) RDF database context. We firstly show how these queries can be defined and implemented in the quantified graph pattern, which is an extension of graph patterns by supporting linguistic quantifier on edges. Then, we develop an algorithm for evaluating quantified RDF graph patterns, that is based on a backtracking strategy which incrementally finds partial solutions by adding joinable candidate vertices or abandoning them when it determines they cannot be completed. Finally, we show some experimental results on real-life data and synthetic data that show the efficiency and scalability of our approach.
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