We demonstrate the option to use the schema-based visual query tool ViziQuer over realistic Linked Data endpoints, with examples over the Semantic Web conference-related Scholarly Data. We present the pipeline of enabling visual query creation over a SPARQL endpoint and ready-to-use data schemas over existing public Linked data endpoints, available in the ViziQuer Schema store. Visual query composition (cf. [1], [2], [3], [4], [5]) along with facet-based ([6], [7]) and controlled natural language-based ([8]) approaches offers a promising avenue to enable end-user involvement in query composition over RDF/SPARQL data (cf. [1]). The recent version of ViziQuer notation ([5], [9]), implemented in a web-based tool3 [10], allows for visual presentation of rich instance level and aggregate queries, involving data expressions, as well as query nesting, with expressive power approaching that of the full SPARQL 1.1 [11]. Meanwhile, the currently available examples of the ViziQuer notation and tool usage are largely related to in-house RDF data stores, not the publicly available Linked data sets that would be one of its primary usage targets. We shall report in this paper and present in the demonstration: Visual query notation and tool usage examples over a public Linked Data endpoint of Scholarly data4 [12]; The pipeline of enabling visual query creation over a SPARQL endpoint, including a novel easy-to-use data schema extractor implementation; Ready-to-use data schemas over existing public Linked data endpoints, available in the ViziQuer Schema store (within the ViziQuer tool page).
The visual queries are defined and constructed in the context of a data schema listing
the available classes and properties and their connectivity, as well as matching the entity
short names to their full IRIs. We present a few examples here based on the data schema
of Scholarly Data [
Each query is a connected graph with one main query node (orange round rectangle).
The linked nodes correspond either to joined classes or to nested queries (if the link
starts with a black bullet). The ViziQuer notation is explained in detail in [
The visual queries over a data endpoint are created within a context of a ViziQuer project that requires a pre-loaded data schema and a specified link to the SPARQL endpoint itself (if the created queries are to be executed from the tool environment).
The ViziQuer data schema can be either generated from an OWL ontology or extracted directly from the SPARQL endpoint (via schema-level SPARQL queries) using an open-source schema extractor service, accompanying the ViziQuer tool. The links to the service are maintained from the ViziQuer tool home page. The novel schema extractor implementation is as a JAVA web service, allowing its seamless usage directly from end-user computers.
There are public data endpoint schemas, extracted and collected at the ViziQuer Schema Store, including Scholarly Data, UNESCO5 (SKOS), Social Semantic Web Thesaurus6 and WikiPathways7. The schemas of resources like DBPedia and WikiData cannot be currently extracted in their full form due to space considerations.
It can also be envisaged that the SPARQL endpoint holders may store and maintain the visual query data schemas, or even the visual project examples along with the endpoints themselves to enable faster users starting up the work with the endpoint.
The visual query environment, after the data schema loading, displays the schema to the end user in the form of a class tree, arranged by the namespaces of the top-level classes and by the subclass relation; this form has been found suitable for moderatelysized data schemas (as the ones, listed in the schema store). A double click on the class tree node adds the main query node with this class into the diagrammatic query pane; further query elements then can be added in the context of the created query element.
There are also options for the data schema export in the form of OWL ontology from the ViziQuer tool, to enable its further analysis or eventual graphic visualization, e.g., in the OWLGrEd ontology editor8. 5 http://vocabularies.unesco.org/sparql 6 http://vocabulary.semantic-web.at/PoolParty/sparql/semweb 7 http://sparql.wikipathways.org/ 8 http://owlgred.lumii.lv/
There is an option to compose visual queries over Linked data endpoints, such as Scholarly Data, in the ViziQuer tool, so allowing the rich visual query experience over Linked Data.
The data schema retrieval as the necessary pre-processing step can be performed by an open-source schema extractor that can be used either from a public server or run on an end-user computer. Further development of the schema extraction service to be able to handle the peculiarities (query execution limits, supported protocols, and SPARQL subsets) of various SPARQL endpoints, is work in progress.
Another avenue of further work is creating an interactive graphical visualization of the data schema within the query tool to ease the end user query creation experience further.