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Querying knowledge graphs through positive and negative examples and feedback

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Abstract

The formulation of structured queries over Knowledge Graphs is not an easy task. To alleviate this problem, we propose a novel interactive method for SPARQL query formulation, for enabling users (plain and advanced) to formulate gradually queries by providing examples and various kinds of positive and negative feedback, in a manner that does not pre-suppose knowledge of the query language or the contents of the Knowledge Graph. In comparison to other example-based query approaches, distinctive features of our approach is the support of negative examples, and the positive/negative feedback on the generated constraints. We detail the algorithmic aspect and we present an interactive user interface that implements the approach. The application of the model on real datasets from DBpedia (Movies, Actors) and other datasets (scientific papers), showcases the feasibility and the effectiveness of the approach. A task-based evaluation that included users that are not familiar with SPARQL, provided positive evidence that the interaction is easy-to-grasp and enabled most users to formulate the desired queries.

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Availability of supporting data

The code of the running prototype, is publicly accessible (https://demos.isl.ics.forth.gr/SPARQL-QBE/). With "View Page Source" one can see all the source code.

Notes

  1. Like the help page of the prototype.

  2. A running prototype is accessible through https://demos.isl.ics.forth.gr/SPARQL-QBE/

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Acknowledgements

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A.A. and Y.T. wrote the main manuscript text and prepared the figures. A.A. implemented the prototype system. All authors reviewed the manuscript.

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Correspondence to Yannis Tzitzikas.

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Akritidis, A., Tzitzikas, Y. Querying knowledge graphs through positive and negative examples and feedback. J Intell Inf Syst 62, 1165–1186 (2024). https://doi.org/10.1007/s10844-024-00846-z

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  • DOI: https://doi.org/10.1007/s10844-024-00846-z

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