# 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:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).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": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/google-knowledge-graph", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Knowledge Graph", "description": "Enrich a list of entity names (people, companies, places, things) with metadata from the Google Knowledge Graph.", "version": "1.0", "x-build-id": "iNNcuigkuRWv7jwxG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~google-knowledge-graph/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-google-knowledge-graph", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~google-knowledge-graph/runs": { "post": { "operationId": "runs-sync-seemuapps-google-knowledge-graph", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~google-knowledge-graph/run-sync": { "post": { "operationId": "run-sync-seemuapps-google-knowledge-graph", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "queries": { "title": "Queries", "type": "array", "description": "List of entity names to look up in the Google Knowledge Graph (people, companies, places, things).", "items": { "type": "string" } }, "limit": { "title": "Limit", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of matches to return per query.", "default": 1 }, "types": { "title": "Types", "type": "array", "description": "Restrict results to these schema.org types (e.g. Person, Organization, Place). Leave empty for any type.", "items": { "type": "string" } }, "languages": { "title": "Languages", "type": "array", "description": "BCP-47 language codes to return results in (e.g. en, es, fr).", "items": { "type": "string" }, "default": [ "en" ] } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```