Flexible querying techniques can enhance users' access to complex, heterogeneous datasets in settings such as Linked Data, where the user may not always know how a query should be formulated in order to retrieve the desired answers. This paper presents query processing algorithms for a fragment of SPARQL 1.1 incorporating regular path queries (property path queries), extended with query approximation and relaxation operators. Our flexible query processing approach is based on query rewriting and returns answers incrementally according to their "distance" from the exact form of the query. We formally show the soundness, completeness and termination properties of our query rewriting algorithm. We also present empirical results that show promising query processing performance for the extended language.

Abstract. Flexible querying techniques can be used to enhance users' access to heterogeneous data sets, such as Linked Open Data. This paper extends SPARQL 1.1 with approximation and relaxation operators that can be applied to regular expressions for querying property paths in order to find more answers than would be returned by the exact form of a user query. We specify the semantics of the extended language and we consider the complexity of query answering with the new operators, showing that both data and query complexity are not impacted by our extensions. We present a query evaluation algorithm that returns results incrementally according to their "distance" from the original query. We have implemented this algorithm and have conducted preliminary trials over the YAGO SPARQL endpoint and the Lehigh University Benchmark, showing promising performance for the language extensions.

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RDF datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQL AR language extends SPARQL 1.1 with two operators — APPROX and RELAX — so as to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics and complexity of SPARQL AR , has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present paper, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQL AR queries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.