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The class of queries for detecting path is an important as those can extract implicit binary relations over the nodes of input graphs. Most of the path querying languages used by the RDF community, like property paths in W3C SPARQL 1.1 and nested regular expressions in nSPARQL are based on the regular expressions. Federated queries allow for combining graph patterns and relational database that enables the evaluations over several heterogeneous data resources within a single query. Federated queries in W3C SPARQL 1.1 currently evaluated over different SPARQL endpoints. In this paper, we present a federated path querying language as an extension of regular path querying language for supporting RDF graph integration with relational database. The federated path querying language is absolutely more expressive than nested regular expressions and negation-free property paths. Its additional expressivity can be used for capturing the conjunction and federation of nested regular path queries. Despite the increase in expressivity, we also show that federated path queries are still enjoy a low computational complexity and can be evaluated efficiently.
Graph query languages feature mainly two kinds of queries when applied to a graph database: those inspired by relational databases which return tables such as SELECT queries and those which return graphs such as CONSTRUCT queries in SPARQL. The latte
We present here a formal foundation for an iterative and incremental approach to constructing and evaluating preference queries. Our main focus is on query modification: a query transformation approach which works by revising the preference relation
The RDF graph-based data model has seen ever-broadening adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey pape
The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given quer
Following the development of fuzzy logic theory by Lotfi Zadeh, its applications were investigated by researchers in different fields. Presenting and working with uncertain data is a complex problem. To solve for such a complex problem, the structure