SPARQL

SPARQL (pronounced “sparkle”) is the standard query language for data stored as RDF, the subject, predicate, object statements that knowledge graphs such as Wikidata are built from. It does for graph data what SQL does for tables. For SEOs, it is the practical way to ask Wikidata direct questions about entities, such as which item matches a Google Knowledge Graph ID or which statements your brand is missing.

How it works

A SPARQL query describes a pattern of triples with variables in it (written with a question mark, such as ?item), and the query engine returns every match in the graph. SELECT returns a table of results; ASK, CONSTRUCT and DESCRIBE return a yes or no answer or new RDF. FILTER, OPTIONAL and UNION let you narrow, extend or combine patterns. The current version, SPARQL 1.1, became a W3C Recommendation on 21 March 2013.

In Wikidata, items have Q identifiers (Q95 is Google) and properties have P identifiers (P856 is official website). The Wikidata Query Service at query.wikidata.org gives you a web editor and a public endpoint that returns JSON or CSV. Its documentation sets a 60-second limit on each query and asks automated clients to send a descriptive User-Agent header.

Example

Say the Knowledge Graph Search API gives you a KGMID and you want the matching Wikidata item. Newer IDs starting with /g/ are stored under property P2671 (Google Knowledge Graph ID), while older /m/ IDs inherited from Freebase sit under P646 (Freebase ID), so the query checks both:

SELECT ?item ?itemLabel WHERE {
  VALUES ?kgmid { "/g/11bxc656v6" }
  { ?item wdt:P2671 ?kgmid } UNION { ?item wdt:P646 ?kgmid }
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}

Run in the Wikidata Query Service in October 2026, this returned Q21708200, OpenAI. Swap in “/m/045c7b” and it returns Q95, Google. If nothing comes back, the entity may still exist in Google’s graph; it just has no Wikidata item carrying that ID.

How it’s used in SEO and marketing

• Entity reconciliation: mapping KGMIDs from entity extraction or the Knowledge Graph Search API to Wikidata items, which you can then use in sameAs links in your schema.
• Entity audits: listing which statements your brand item has (official website, founders, industry, social profiles) and which are missing or outdated.
• Competitor and gap analysis: pulling every item of a type in your sector and comparing how well each is described.
• Agentic retrieval: MLforSEO’s AI Search Optimization and Agentic SEO course notes that agents can query structured data directly, including through a knowledge graph SPARQL endpoint.

Limitations and things to watch

• There is a learning curve, and queries use IDs rather than names, so keep a list of the Q and P numbers you use.
• Wikidata is community-edited and incomplete. A missing or wrong KGMID statement says nothing about Google’s own graph.
• Google’s Knowledge Graph itself has no SPARQL endpoint. Use the Knowledge Graph Search API for that.
• The public endpoint times out and throttles heavy use, so keep queries tight and cache results.

Learn it on MLforSEO

• Wikidata for Brands: Notability Criteria and a Realistic Path
• What Is a Knowledge Graph? How to Use It for Smarter Keyword Strategy
• Entity Disambiguation in Practice
• The AI-powered Entity Audit (course)
• AI Search, LLMs, Entity SEO and Knowledge Graph Strategies for Brands (course)