Applications of flexible querying to graph data

Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information seeking requirements. There is a need therefore for flexible querying systems over graph data that can automatically make changes to the user's query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations

Flexible query processing of SPARQL queries

SPARQL is the predominant language for querying RDF data, which is the standard model for representing web data and more specifically Linked Open Data (a collection of heterogeneous connected data). Datasets in RDF form can be hard to query by a user if she does not have a full knowledge of the structure of the dataset. Moreover, many datasets in Linked Data are often extracted from actual web page content which might lead to incomplete or inaccurate data. We extend SPARQL 1.1 with two operators, APPROX and RELAX, previously introduced in the context of regular path queries. Using these operators we are able to support exible querying over the property path queries of SPARQL 1.1. We call this new language SPARQLAR. Using SPARQLAR users are able to query RDF data without fully knowing the structure of a dataset. APPROX and RELAX encapsulate different aspects of query flexibility: finding different answers and finding more answers, respectively. This means that users can access complex and heterogeneous datasets without the need to know precisely how the data is structured. One of the open problems we address is how to combine the APPROX and RELAX operators with a pragmatic language such as SPARQL. We also devise an implementation of a system that evaluates SPARQLAR queries in order to study the performance of the new language. We begin by defining the semantics of SPARQLAR and the complexity of query evaluation. We then present a query processing technique for evaluating SPARQLAR queries based on a rewriting algorithm and prove its soundness and completeness. During the evaluation of a SPARQLAR query we generate multiple SPARQL 1.1 queries that are evaluated against the dataset. Each such query will generate answers with a cost that indicates their distance with respect to the exact form of the original SPARQLAR query. Our prototype implementation incorporates three optimisation techniques that aim to enhance query execution performance: the first optimisation is a pre-computation technique that caches the answers of parts of the queries generated by the rewriting algorithm. These answers will then be reused to avoid the re-execution of those sub-queries. The second optimisation utilises a summary of the dataset to discard queries that it is known will not return any answer. The third optimisation technique uses the query containment concept to discard queries whose answers would be returned by another query at the same or lower cost. We conclude by conducting a performance study of the system on three different RDF datasets: LUBM (Lehigh University Benchmark), YAGO and DBpedia