Experience with Visual SPARQL Queries over DBPedia

Material to support a paper at VOILA! 2022

Graphically construct and execute rich data analysis queries over DBPedia and other RDF Data Sets.

Data instance level and aggregate queries supported

ViziQuer tool Consult the ViziQuer main page for tool usage instructions and context information.

Query environment Open https://viziquer.app.
Google Chrome browser is recommended (may work also in other browsers).

Register (sign up) as a new user (email checking not done, any e-mail-like string is accepted), then log in.

Create a new project (give at least the name for the project), initialize your project by the predefined DBPedia QALD project ('Initialise by DBPedia QALD 9 queries'). If an empty project is created, its contents can be uploaded (button 'Upload Project') later from a sample project file or URL, as e.g., DBPedia QALD 9 sample project.

The diagrams with QALD 9 queries shall be loaded and can be clicked to open. New diagrams can be created, as well. The tabs for classes, properties and individuals shall be to the right of the diagram. Double-click an item in a list to seed a query from it.

Demonstration Live interaction with the examples is welcome within the query environment (use 'DBPedia QALD 9 queries' for the new project initialization).

Library of Visual QALD-9 Queries Available as a project within the query environment.

The project file can be separately downloaded (in JSON-based abstract syntax).

Analysis of DBPedia QALD example SPARQL query visualizations (.pdf)

The original QALD-9 queries are available here.

User study The participants were recruited from students of Knowledge Engineering course at Master's degree programme at University of Latvia. The students have had a limited previous experience with ViziQuer by an introduction during a lecture and solving a homework with 16 visual query composition over the hospital data endpoint.
The students were handed out 9 query formulations in natural language and they were asked to create the visual query formulations. There were no time pressure on the participants, as the queries were asked to be filled in the form of an extra homework.

The study results are summarized here
(successful queries by students (out of 9): 9,9,8,8,8,8,7,6,5,5;
successful students per query (out of 10): 10,10,10,10,9,9,6,6,3);
the problems arising from not using the best properties corresponding to the informally stated terms, as discussed in the paper).

Open Source The branch dss-based-schemata within Viziquer repository in GitHub contains the code of the visual tool version that works with a separate data shape server.

The code for data shape server (serving the names for auto-completion) is in its own repository on GitHub.

The ViziQuer main page describes open source tools for data schema population (retrieval), as well.