# Google Knowledge Graph (`seemuapps/google-knowledge-graph`) Actor

Enrich a list of entity names (people, companies, places, things) with metadata from the Google Knowledge Graph.

- **URL**: https://apify.com/seemuapps/google-knowledge-graph.md
- **Developed by:** [Andrew](https://apify.com/seemuapps) (community)
- **Categories:** SEO tools, Agents, Developer tools
- **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 entity enricheds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Google Knowledge Graph

Turn any list of entity names - companies, people, places, products - into structured metadata pulled straight from the Google Knowledge Graph. No scraping, no parsing, no guessing.

### What you get

- **One dataset row per match** with entity ID, canonical name, schema.org types, short and long descriptions, official URL, and image URLs
- **Configurable match limit** (1-20) per query - top match by default, or fetch the full candidate list for disambiguation
- **Type filtering** to constrain results to `Person`, `Organization`, `Place`, or any schema.org type
- **Multi-language support** - request results in any BCP-47 language
- **Result scores** for confidence-weighted downstream logic
- Error-tolerant - failed lookups produce an error row and the run continues

### Example output

```json
{
  "query": "Anthropic",
  "rank": 1,
  "status": "success",
  "entityId": "kg:/g/11h3pnwf3p",
  "name": "Anthropic",
  "types": ["Corporation", "Organization", "Thing"],
  "description": "Artificial intelligence company",
  "detailedDescription": "Anthropic is an American artificial intelligence startup company...",
  "detailedDescriptionUrl": "https://en.wikipedia.org/wiki/Anthropic",
  "imageUrl": "https://upload.wikimedia.org/...png",
  "imageContentUrl": "https://en.wikipedia.org/wiki/File:Anthropic_logo.svg",
  "url": "https://www.anthropic.com/",
  "resultScore": 1432.51,
  "error": null
}
```

### Use cases

- **CRM enrichment** - turn a column of company names into a structured profile with logos, descriptions, and official URLs
- **Lead scoring** - use `resultScore` and entity `types` to filter out generic terms and prioritise real organisations
- **"Is this a real company?" verification** - separate well-known entities from misspellings, shell companies, or unknown brands before outreach
- **Content categorisation** - tag articles, products, or tickets with schema.org types from authoritative entity matches
- **Entity disambiguation for ML pipelines** - resolve ambiguous strings ("Apple", "Mercury") to a canonical Knowledge Graph ID before downstream processing
- **Knowledge base seeding** - bootstrap a graph of people, companies, and places with descriptions and images sourced from Wikipedia and Wikidata

### How to use

1. Paste your entity names into the **Queries** field - one per entry
2. Set **Limit** (default 1 - top match only; raise to 5-10 for disambiguation workflows)
3. (Optional) Restrict **Types** to filter results, e.g. `Organization`, `Person`, `Place`
4. (Optional) Set **Languages** for localised names and descriptions (default `en`)
5. Run the actor - every match appears as its own dataset row, ready to export as CSV or JSON

### Output schema

| Field | Type | Description |
|---|---|---|
| `query` | string | The input query |
| `rank` | number | 1-based rank within the query (0 if no matches or error) |
| `status` | string | `"success"` or `"error"` |
| `entityId` | string | null | Knowledge Graph ID (e.g. `kg:/m/0d3kx`) |
| `name` | string | null | Canonical entity name |
| `types` | string\[] | Schema.org `@type` values |
| `description` | string | null | Short description |
| `detailedDescription` | string | null | Long-form description (typically Wikipedia intro) |
| `detailedDescriptionUrl` | string | null | Source URL of the detailed description |
| `imageUrl` | string | null | Direct image URL (`image.contentUrl`) |
| `imageContentUrl` | string | null | Image source page (`image.url`) |
| `url` | string | null | Entity's official URL |
| `resultScore` | number | null | Google's relevance score |
| `error` | string | null | Error message if status is `"error"`, or `"no matches"` |

If a query fails, `status` is `"error"` and `error` contains the reason - the run continues with the remaining queries.

### Tips

- For CRM enrichment, set **Limit** to 1 - you almost always want the top match
- For disambiguation, set **Limit** to 5-10 and inspect `resultScore` and `types`
- Filter by `types: ["Organization"]` to skip people and places when enriching company lists
- The Google Knowledge Graph favours well-known entities - obscure local businesses may return no matches

# Actor input Schema

## `queries` (type: `array`):

List of entity names to look up in the Google Knowledge Graph (people, companies, places, things).

## `limit` (type: `integer`):

Maximum number of matches to return per query.

## `types` (type: `array`):

Restrict results to these schema.org types (e.g. Person, Organization, Place). Leave empty for any type.

## `languages` (type: `array`):

BCP-47 language codes to return results in (e.g. en, es, fr).

## Actor input object example

```json
{
  "queries": [
    "Anthropic",
    "OpenAI",
    "Apify"
  ],
  "limit": 1,
  "languages": [
    "en"
  ]
}
```

# Actor output Schema

## `results` (type: `string`):

One record per match: query, rank, status (success|error), entityId, name, types, description, detailedDescription, detailedDescriptionUrl, imageUrl, imageContentUrl, url, resultScore, error.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "queries": [
        "Anthropic",
        "OpenAI",
        "Apify"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("seemuapps/google-knowledge-graph").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "queries": [
        "Anthropic",
        "OpenAI",
        "Apify",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("seemuapps/google-knowledge-graph").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "queries": [
    "Anthropic",
    "OpenAI",
    "Apify"
  ]
}' |
apify call seemuapps/google-knowledge-graph --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,seemuapps/google-knowledge-graph"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/6hzG7jkHUi3dgfyRW/builds/xtYl1tVD3wA8WJMHh/openapi.json