SPARQL queries for the Semantic Web Working Group

100%

SPARQL queries for the Semantic Web Working Group

✓ Published0🌍 Public

Last edited Jan 30, 2018

Created on Jan 22, 2018

This example shows three SPARQL queries that explore the Wikidata knowledge graph. One query counts paintings containing cats versus dogs to test a hypothesis about which animal appears more frequently. Another generates an ImageGrid view of Rembrandt’s artworks using the defaultView directive, while a third lists Spanish painters and sample images of their work. The queries use standard SPARQL syntax with property paths (wdt:P135/wdt:P279?), label service calls, and BIND for computed values. Results render directly in Wikidata’s query service interface.

AI-generated description

Similar vizzes

Loading thumbnail…

Wikidata for Theological Librarians

This example demonstrates how theological librarians can use Wikidata to explore and visualize interconnected scholarly data. It shows a SPARQL query interface that retrieves information about theologians, their works, and associated religious concepts, rendered as an interactive network graph. The visualization uses D3.js’s force-directed layout to position nodes and links, with hover tooltips revealing entity details. Data is sourced live from Wikidata’s public endpoint, while the code employs the `d3.forceSimulation` API for physics-based layout and custom SVG rendering for clarity.

CCliffordAnderson

61% match

Loading thumbnail…

Simple DBpedia SPARQL Query

This example demonstrates a SPARQL query against DBpedia, retrieving titles, publication dates, and page counts for books authored by David Foster Wallace. The query uses the `d:` and `dbpedia2:` namespace prefixes to access DBpedia ontology and property data, with results ordered by publication date. The code defines a `PREFIX` for the DBpedia ontology and a `SELECT` clause that binds three variables—`?title`, `?pubDate`, and `?pages`—from matching book resources. It relies on DBpedia’s SPARQL endpoint and standard RDF predicates like `rdfs:label` to fetch the displayed fields. The visualization itself is not shown in the snippet; only the query logic and data-fetching structure are provided.

CCliffordAnderson

60% match

Loading thumbnail…

Influential Artists

This visualization maps a subset of Wikidata’s “influenced by” relationships among artists as a force-directed graph. Nodes represent individual artists, and links connect each artist to those who influenced them, revealing clusters of artistic heritage and cross-disciplinary connections. The graph is rendered using Neo4j’s browser with the `graph_gist_artists.adoc` file, which seeds the database with Cypher `CREATE` statements and applies a blue color style to all `Artist` nodes. Data is sourced from a SPARQL query targeting instances of the “artist” occupation and the `P737` property.

CCliffordAnderson

58% match

Loading thumbnail…

Example SPARQL Query for DBPedia (Source: https://github.com/gklyne/asqc)

This example shows a SPARQL query that retrieves data about English musicians born in Exeter from DBPedia, returning their names, birth dates, and English-language descriptions. The query uses the DBPedia ontology and property vocabulary (such as `dbo:birthPlace` and `dbpedia:Exeter`) along with standard RDF and FOAF predicates. It filters descriptions by language using `FILTER (LANG(?description) = 'en')` to ensure English results. The query relies on the SPARQL endpoint’s built-in support for DBPedia’s RDF triples, with no additional visualization library involved.

CCliffordAnderson

57% match

Loading thumbnail…

Wikidata RPC for NetsBlox

This visualization demonstrates how Wikidata can be accessed as a Remote Procedure Call (RPC) service within NetsBlox, a visual programming environment. Built with Vue and rendered on HTML5 Canvas, the project pairs an XML room definition with a markdown guide that explains Wikidata's code-block integration. The example uses WebGL for rendering and focuses on Berlin-related Wikidata queries, showing real-time data retrieval from the live knowledge base directly within NetsBlox's block-based interface. The visualization is accompanied by an animated GIF illustrating the code blocks in action, making it a practical reference for developers working with semantic data in educational or prototyping contexts.

CCliffordAnderson

57% match

Loading thumbnail…

Artist Networks from WIkidata

This visualization maps the influence network of artists and thinkers from Wikidata, showing directed relationships from influencers to those they influenced. The code transforms the JSON data into a graph structure using XQuery, extracting entity IDs and labels to define nodes and edges. It uses the fetch and json:parse libraries to load and parse the data, then constructs Cypher-like statements for nodes and relationships. The resulting network highlights how figures like Copernicus and Strindberg connect to their intellectual predecessors.

CCliffordAnderson

56% match

Loading thumbnail…

Loading data through SPARQL

This example demonstrates how to query a live SPARQL endpoint using d3.json, fetching up to 1000 records created in 1893 from the Dutch Network of Digital Heritage. The results are displayed as raw JSON text in a paragraph element. The code encodes a SPARQL query with encodeURIComponent and appends it to the endpoint URL, then parses the response with d3’s JSON loader. It relies on d3.v5 and the public API endpoint, which must remain active for the example to function.

LLaurens

55% match

Loading thumbnail…

Transforming data using d3

This example demonstrates how to clean, group, and enrich SPARQL query results about ethnographic artefacts. It loads data from a Dutch heritage endpoint using `d3.json`, maps each row to extract plain values with `cleanData`, then applies `d3.nest()` to group artefacts by their parent country. For each country group, it calculates the total count of artefacts as a new `amount` property. The code uses d3 v5 APIs such as `d3.nest()` and `d3.json`, and logs the raw, cleaned, and transformed data to the console for inspection.

LLaurens

54% match