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Automatically Creating Benchmarks for RDF Keyword Search Evaluation

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Abstract

Keyword search systems provide users with a friendly alternative to access Resource Description Framework (RDF) datasets. Evaluating such systems requires adequate benchmarks, consisting of RDF datasets, keyword queries, and correct answers. However, available benchmarks often have small sets of queries and incomplete sets of answers, mainly because they are manually constructed with the help of experts. The central contribution of this article is an offline method to build benchmarks automatically, allowing larger sets of queries and more complete answers. The proposed method has two parts: query generation and answer generation. Query generation extracts keywords for each entity from a selected set of relevant entities, called inducers, and heuristics guide the process of extracting possible keywords related to each inducer. Answer generation takes the queries and computes solution generators (SG), which are subgraphs of the original dataset containing different answers to a query. Heuristics also guide the process by building SGs only for the relevant answers.

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Notes

  1. Benchmark available at https://dataverse.lib.virginia.edu/file.xhtml?fileId=1166&version=1.0

  2. http://qald.aksw.org

  3. http://qald.aksw.org

  4. http://www.dbis.informatik.uni-goettingen.de/Mondial/

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Acknowledgements

This work was partly funded by FAPERJ under grant E-26/202.818/2017; by CAPES under grants 88881.310592-2018/01, 88881.134081/2016-01, and 88882.164913/2010-01 and by CNPq under grant 302303/2017-0. We are grateful to João Guilherme Alves Martinez for helping with the experiments.

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Authors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil

    Angelo B. Neves, Yenier T. Izquierdo, Javier G. Jiménez & Marco A. Casanova

  2. Universidade Federal Fluminense, Niterói, RJ, Brazil

    Luiz André P. Paes Leme

  3. Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

    Giseli R. Lopes

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  1. Angelo B. Neves
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Correspondence to Angelo B. Neves.

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This article is part of the topical collection “Enterprise Information Systems” guest edited by Michal Smialek, Slimane Hammoudi, Alexander Brodsky and Joaquim Filipe.

Appendices

Appendix A. Queries for \(\mathcal{K}_{\textit{Coff-Q43}}=\{{\hbox{``}}mauritius{\hbox{''}},{\hbox{``}}india{\hbox{''}}\}\)

Answer graph pattern

Query for retrieving property values

Query to generate the Coffman’s ground truth

Appendix B. Queries for \(\mathcal{K}_{\textit{Coff-Q30'}}=\{{\hbox{``}}polland{\hbox{''}},{\hbox{``}}spoken{\hbox{''}},{\hbox{``}}language{\hbox{''}}\}\)

Answer graph pattern

Query for retrieving property values

Query to generate the Coffman’s ground truth

Appendix C. Queries for \(\mathcal{K}_{\textit{Coff-Q27}}=\{{\hbox{``}}nigeria{\hbox{''}},{\hbox{``}}gdp{\hbox{''}}\}\)

Answer graph pattern

Query for retrieving property values

Query to generate the Coffman’s ground truth

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Neves, A.B., Leme, L.A.P.P., Izquierdo, Y.T. et al. Automatically Creating Benchmarks for RDF Keyword Search Evaluation. SN COMPUT. SCI. 3, 312 (2022). https://doi.org/10.1007/s42979-022-01100-5

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