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    Optimisation techniques for flexible SPARQL queries

    Frosini, Riccardo and Poulovassilis, Alex and Wood, Peter and Cali, Andrea (2022) Optimisation techniques for flexible SPARQL queries. ACM Transactions on the Web , ISSN 1559-1131. (In Press)

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    Abstract

    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.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Peter Wood
    Date Deposited: 04 Jul 2022 10:34
    Last Modified: 06 Jul 2022 05:46
    URI: https://eprints.bbk.ac.uk/id/eprint/48551

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