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Knowledge Graph & Entities

Entity extraction, relationship mapping, enrichment and querying the knowledge base built from your searches.

What the knowledge graph stores

Every search contributes to a knowledge graph. Named entity recognition identifies people, companies, places and other entities in results; relationship mapping then links them — who works where, which company owns what, how events connect. Over time this becomes a knowledge base you can query directly, independent of any single search.

Entity extraction

Named entity recognition pulls people, organizations, locations and more out of search results automatically.

Relationship mapping

Entities are connected by typed relationships, so you can traverse from a person to their company to its competitors.

Knowledge base queries

Ask questions against the accumulated graph itself — useful for recurring research subjects.

Entity enrichment

Recognized entities are enriched with additional data layers so a profile is more than a name and a description.

Enrichment layerWhat it adds
FinancialCompany financials and market data, including live stock prices for public companies
ProfessionalRoles, affiliations and organizational connections
PersonalPublic biographical details from encyclopedic and news sources
IdentityDisambiguation data that distinguishes entities sharing the same name