Knowledge Graph API

Retrieve a specific entity using its unique machine ID (MID) — a stable, language-independent identifier that persists across Knowledge Graph updates. This allows for deterministic entity verification without ambiguity.

• Query format: https://kgsearch.googleapis.com/v1/entities:search?ids=/m/0c0194
• Returns canonical name, description, image URL, and detailed description
• MID remains constant even when entity names or descriptions change

The computational process of resolving a textual mention to its unique machine ID (MID) in the Knowledge Graph. When a query contains an ambiguous term like 'Mercury,' the API uses contextual signals—surrounding entities, prior query history, and co-occurrence statistics—to distinguish between the planet, the element, and the musician. This is critical for conversational AI and semantic search where precision depends on knowing exactly which entity is being referenced.

The systematic process of matching internal enterprise data records against the Knowledge Graph's canonical entities. Key steps include:

• Fuzzy string matching on entity names and aliases
• Attribute comparison using schema.org types and properties
• Confidence scoring to accept or reject matches
• MID assignment to link internal records to Google's unique identifiers

This process transforms unstructured CRM data into a semantically linked knowledge base that AI systems can query with precision.

The practice of using the schema.org 'sameAs' property to explicitly assert that a brand's website represents the same entity as its Knowledge Graph entry. When implemented correctly, this creates an unbroken chain of identity: Website → Wikidata → Knowledge Graph API. The API's entity search endpoint can then be queried programmatically to verify that Google has correctly ingested and linked these assertions, confirming entity recognition at the machine level.

Google's automated, probabilistic knowledge base that feeds the Knowledge Graph. Unlike curated sources like Wikidata, the Knowledge Vault extracts triple assertions (subject-predicate-object) directly from unstructured web text using machine learning. The Knowledge Graph API exposes the high-confidence outputs of this system. Understanding the Vault's extraction mechanisms helps predict which entity attributes will surface via the API and which require explicit reinforcement through structured data markup.

The algorithmic assignment of importance scores to entities within the Knowledge Graph. Factors influencing node weight include:

• Inbound link count from other high-weight entities
• Search query volume and click-through rates
• Freshness of associated web content
• Citation frequency in authoritative sources

The Knowledge Graph API's resultScore field reflects this weighting, indicating how prominently an entity ranks relative to others with similar names. Higher scores correlate with greater visibility in Knowledge Panels and AI-generated answers.

A technique that transforms the nodes and edges of the Knowledge Graph into dense vector representations in high-dimensional space. These embeddings encode semantic relationships—entities with similar attributes or connection patterns cluster together. The Knowledge Graph API provides the raw structured data; graph embeddings derived from it enable similarity search, recommendation engines, and entity linking in custom machine learning pipelines. Tools like PyKEEN and AmpliGraph can generate embeddings from API-exported subgraphs.