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How is Your Knowledge Graph Used: Content-Centric Analysis of SPARQL Query Logs

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14265))

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Abstract

Knowledge graphs (KGs) are used to integrate and persist information useful to organisations, communities, or the general public. It is essential to understand how KGs are used so as to evaluate the strengths and shortcomings of semantic web standards, data modelling choices formalised in ontologies, deployment settings of triple stores etc. One source of information on the usage of the KGs is the query logs, but making sense of hundreds of thousands of log entries is not trivial. Previous works that studied available logs from public SPARQL endpoints mainly focused on the general syntactic properties of the queries disregarding the semantics and their intent. We introduce a novel, content-centric, approach that we call query log summarisation, in which we group the queries that can be derived from some common pattern. The type of patterns considered in this work is query templates, i.e. common blueprints from which multiple queries can be generated by the replacement of parameters with constants. Moreover, we present an algorithm able to summarise a query log as a list of templates whose time and space complexity is linear with respect to the size of the input (number and dimension of queries). We experimented with the algorithm on the query logs of the Linked SPARQL Queries dataset showing promising results.

An extended version of this paper (pre-print) is available at https://doi.org/10.6084/m9.figshare.23751243.

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Notes

  1. 1.

    http://usewod.org/workshops.html.

  2. 2.

    Using the initial underscore in the variable name to identify parameters matches with existing practice [27], while using “$” visually helps distinguish the parameters from query variables that often start with “?”.

  3. 3.

    For brevity, the queries omit prefix declarations:

    • dbr: <http://dbpedia.org/resource/>

    • rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

    • foaf: <http://xmlns.com/foaf/0.1/>

    • dbo: <https://dbpedia.org/ontology/>

    .

  4. 4.

    It is for example a recommended way to perform query federation [34].

  5. 5.

    https://github.com/RubenVerborgh/SPARQL.js.

  6. 6.

    https://jena.apache.org/documentation/fuseki2.

  7. 7.

    https://github.com/miguel76/sparql-clustering.

  8. 8.

    http://lsq.aksw.org/.

  9. 9.

    In the table, for conciseness, the statistics of the Bio2RDF endpoints are shown only aggregated for the whole project. In Appendix B the extended version of the paper there is a more detailed version of the table showing the statistics endpoint by endpoint..

  10. 10.

    This choice is motivated by the fact that the Bio2RDF endpoints are part of the same project, the collected logs refer roughly to the same period, and there is considerable overlap in the clients querying the endpoints.

  11. 11.

    With the exception of the Bio2RDF endpoints, which are considered as a whole.

  12. 12.

    One counts the triples in which one resource is subject and the other object, the other counts the triples in which they replace each other or have symmetric role.

  13. 13.

    https://doi.org/110.6084/m9.figshare.23751138.

  14. 14.

    http://lsq.aksw.org/.

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Acknowledgements

This work was partially supported by the PNRR project “Fostering Open Science in Social Science Research (FOSSR)” (CUP B83C22003950001) and by the PNRR MUR project PE0000013-FAIR.

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Authors and Affiliations

  1. University of Bologna, Via Zamboni 33, Bologna, Italy

    Luigi Asprino

  2. University of Bari Aldo Moro, Via Orabona 4, Bari, Italy

    Miguel Ceriani

  3. ISTC-CNR, Via S. Martino della Battaglia 44, Roma, Italy

    Miguel Ceriani

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  1. Luigi Asprino
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Correspondence to Miguel Ceriani .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, Beijing, China

    Juanzi Li

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Asprino, L., Ceriani, M. (2023). How is Your Knowledge Graph Used: Content-Centric Analysis of SPARQL Query Logs. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_11

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