JSON-LD multilingual support explained: How to describe multiple languages at the same time

In the development of international web applications and multilingual websites, multilingual adaptation of structured data is a critical step to enhance search engine visibility and optimize cross-lingual user experience. As a recommended structured data format by Schema.org, JSON-LD (JSON for Linked Data) supports multiple ways to describe multilingual content through its flexible syntax, effectively addressing the core need of “simultaneously presenting multiple language versions of the same property”. This article will start from practical application scenarios, detail the implementation methods, considerations, and optimization tips for JSON-LD multilingual support, helping developers build a search engine-compliant structured data system for multilingual websites, cross-border e-commerce platforms, and international news sites.

1. Why Must Multilingual Websites Implement JSON-LD Multilingual Support?

For websites targeting global users (such as cross-border e-commerce platforms, international education websites, and multilingual news sites), JSON-LD multilingual support is not an optional feature but a core factor affecting SEO rankings and user retention. Its core value is reflected in three dimensions:

1.1 Improve Multilingual Search Engine Rankings and Traffic

Search engines (e.g., Google, Bing) can identify the relevance of content in different languages through multilingual tags in JSON-LD. When users search in non-Chinese languages (such as English or Japanese), the search engine can accurately match the corresponding language version of the page. For example, if the “product name” of a cross-border e-commerce platform is tagged with both Chinese (zh) and English (en), when an English user searches for “Example Product”, the search engine can directly retrieve the English version from JSON-LD and prioritize displaying the page—avoiding ranking drops caused by “language mismatch”.

1.2 Adapt to Voice Search and AI Assistant Requirements

Current AI assistants like Google Assistant and Siri all support multilingual voice interaction. If JSON-LD lacks language tags, the assistant cannot identify the language attribute of the content, which may lead to issues such as “returning a Chinese response to an English query”. Through JSON-LD multilingual tagging, AI assistants can quickly extract structured data (e.g., product descriptions, service introductions) in the corresponding language and directly generate voice responses that match the user’s language habits, improving interaction efficiency.

1.3 Ensure Consistent Cross-Lingual User Experience

Multilingual users prefer to read content in their native language. JSON-LD multilingual tagging ensures that structured data (e.g., breadcrumb navigation, product parameters, article summaries) is synchronized with the visible content of the page. For instance, when a Chinese user sees “ 示例产品 ” (Example Product), JSON-LD outputs the Chinese tag simultaneously; when an English user switches the language, JSON-LD automatically matches the English tag. This avoids contradictions like “the page displays English but the structured data remains in Chinese” and enhances user trust.

2. 3 Core Implementation Methods for JSON-LD Multilingual Support (with Code Examples)

Based on the complexity of multilingual content (basic text/complex structure), JSON-LD offers three mainstream implementation methods. Developers can choose the appropriate solution based on their scenarios. The following examples all use a “cross-border e-commerce product page” (with Schema.org type Product) and cover the common multilingual needs of “name” and “description” properties.

Method 1: Use the @language Keyword (Preferred for Basic Text)

@language is a natively supported language tagging keyword in JSON-LD. It binds multiple language versions to the same property through an “array + object” format, making it suitable for scenarios that require only simple text descriptions (e.g., product names, short summaries). It is compatible with all major search engines.

Code Example: Implement Product Multilingual Tagging with the @language Keyword

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": [
    {"@value": "Example Product (Chinese)",
      "@language": "zh-CN"  // Simplified Chinese, with regional variant distinction
    },
    {"@value": "Example Product (English)",
      "@language": "en-US"  // American English, adapted for regional needs
    },
    {
      "@value": "製品例（日本語）",
      "@language": "ja"     // Japanese, no regional variant (uses language code directly)
    }
  ],
  "description": [
    {
      "@value": "This is an example product for cross-border e-commerce, supporting international shipping.",
      "@language": "zh-CN"
    },
    {
      "@value": "This is an example product for cross-border e-commerce, supporting international shipping.",
      "@language": "en-US"
    },
    {
      "@value": "これはクロスボーダー EC 向けのサンプル製品で、国際配送に対応しています。",
      "@language": "ja"
    }
  ],
  "brand": {
    "@type": "Brand",
    "name": [
      {"@value": "Example Brand (Chinese)",
        "@language": "zh-CN"
      },
      {"@value": "Example Brand (English)",
        "@language": "en-US"
      }
    ]
  }
}

Key Notes:

• Language Code Standards: Follow the ISO 639-1 standard (2 lowercase letters, e.g., zh, en). For regional variants, append the ISO 3166-1 alpha-2 regional code (e.g., zh-CN, en-GB). Avoid non-standard codes (e.g., “cn”, “ch”).
• Nested Property Support: Not only top-level properties (name, description) but also nested properties (e.g., brand.name) can implement multilingual support via @language, ensuring consistent language across the entire data chain.

Method 2: Use Schema.org’s Language Type (Concise and Efficient)

Schema.org provides a dedicated Language type, which allows direct binding of “language code-text content” key-value pairs to properties. It is suitable for scenarios with fewer language versions and a focus on code simplicity (e.g., bilingual websites) and eliminates the need for nested arrays, resulting in more readable code.

Code Example: Implement Bilingual Product Tagging with the Language Type

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": {
    "@type": "Language",
    "zh-CN": "Example Product (Bilingual Version)",
    "en-US": "Bilingual Example Product",
    "zh-TW": "示例產品（繁體版）"  // Supports Traditional Chinese (Taiwan region)
  },
  "description": {
    "@type": "Language",
    "zh-CN": "This product supports Chinese and English descriptions, suitable for Mainland China and North American markets.",
    "en-US": "This product supports Chinese and English descriptions, suitable for Mainland China and North American markets.",
    "zh-TW": "本產品支援中英雙語說明，適配台灣地區與北美市場。"
  },
  "offers": {
    "@type": "Offer",
    "name": {
      "@type": "Language",
      "zh-CN": "Limited-Time Discount",
      "en-US": "Limited-Time Discount"
    },
    "price": "99.99"
  }
}

Key Notes:

• Syntax Simplicity: Use language codes (e.g., zh-CN, en-US) as keys and text as values directly, avoiding array nesting. This is ideal for rapid development of bilingual or trilingual pages.
• Compatibility Tip: Some older search engines may have limited support for the Language type. It is recommended to verify using Google’s Rich Results Test (mentioned below) to ensure data can be crawled normally.

Method 3: Use the ItemList Type (Adapted for Complex Structures)

When multilingual content requires additional metadata (e.g., content update time, author, regional restrictions), you can nest Language objects via the ItemList type to implement combined descriptions of “language content + metadata”. This is suitable for complex scenarios (e.g., multilingual news, product manuals with copyright information).

Code Example: Implement Multilingual Tagging with Metadata Using the ItemList Type

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": {
    "@type": "ItemList",
    "itemListElement": [
      {
        "@type": "Language",
        "name": "Example Product (with Update Time)",  // Chinese name
        "language": "zh-CN",
        "dateModified": "2024-05-20",   // Update time for the Chinese version
        "author": {
          "@type": "Person",
          "name": "Chinese Product Team"
        }
      },
      {
        "@type": "Language",
        "name": "Example Product (with Update Time)",  // English name
        "language": "en-US",
        "dateModified": "2024-05-19",  // Update time for the English version
        "author": {
          "@type": "Person",
          "name": "English Product Team"
        }
      }
    ]
  },
  "description": {
    "@type": "ItemList",
    "itemListElement": [
      {
        "@type": "Language",
        "description": "This product description was updated in May 2024, adding warranty policy details.",
        "language": "zh-CN",
        "dateModified": "2024-05-20"
      },
      {
        "@type": "Language",
        "description": "This product description was updated in May 2024, adding warranty policy details.",
        "language": "en-US",
        "dateModified": "2024-05-19"
      }
    ]
  }
}

Key Notes:

• Metadata Extension: You can add Schema.org-supported properties (e.g., dateModified for update time, author for the author, publisher for the publisher) to the Language object to meet complex scenario requirements.
• Applicable Scenarios: Sites where multilingual content has different update frequencies or requires copyright labeling (e.g., international news platforms, multilingual technical documents) should prioritize this method.

3. JSON-LD Multilingual Support Pitfall Avoidance Guide (4 Core Considerations)

Even if you master the implementation methods, neglecting details may cause search engines to fail to recognize the data or lead to fragmented user experiences. The following 4 considerations must be strictly followed:

1. Language Codes Must Comply with International Standards

• Basic Specifications: Use ISO 639-1 for language codes (2 lowercase letters, e.g., zh, en, ja). For regional variants, use the “language code-regional code” format (e.g., zh-CN, en-GB, es-ES). Avoid using Chinese names (e.g., “ 中文 ”) or non-standard codes (e.g., “cn”, “us”).
• Common Error Comparison:

2. Structured Data Must Be Language-Synchronized with Page Content

• Core Principle: Multilingual content in JSON-LD must be completely consistent with the visible content of the page. Avoid contradictions like “the page displays English but JSON-LD is in Chinese”. For example, after switching the website to English, the name “Example Product” in JSON-LD must exactly match the page title. Otherwise, search engines may judge it as “inaccurate data” and affect rankings.
• Inspection Method: When switching the website’s language version, check the data via “Browser Developer Tools > Application > JSON-LD” to confirm that the language matches the page content.

3. Prioritize Implementation Methods Supported by Search Engines

• Compatibility Ranking: @language keyword > Language type > ItemList type. The @language keyword is a native JSON-LD property and is fully supported by all major search engines (Google, Bing, Yandex), so it is recommended as the first choice. The ItemList type should only be used when additional metadata is needed to avoid unnecessary complexity.
• Verification Tool: After completing all implementations, you must verify using Google’s Rich Results Test (Visit Here) to check if “multilingual content” is correctly identified (the tool will display property values for each language version).

4. Ensure Semantic Consistency of Multilingual Content

• Translation Requirements: Content in different languages must be “semantically equivalent” to avoid translation errors or information omissions. For example, the Chinese description “ 支持全球联保 ” (Supports global warranty) should correspond to “Supports global warranty” in English, not “Supports international shipping” (semantic deviation).
• Tool Recommendations: Use professional translation tools (e.g., DeepL, Google Translate Professional Edition) for assistance, and have native speakers proofread to ensure accuracy.

4. Conclusion: Scenario-Based Selection Strategy for JSON-LD Multilingual Support

Based on the complexity of multilingual website requirements, you can choose an implementation method that balances development efficiency and effectiveness:

By choosing the right method and strictly following specifications, JSON-LD multilingual support can bring two core benefits to multilingual websites: first, it helps search engines accurately identify content in each language version and improve cross-lingual search rankings; second, it ensures that the content seen by users is synchronized with the language of the structured data, optimizing the experience for native-language users.

Appendix: Reference Resources for JSON-LD Multilingual Support

1. Schema.org Official Multilingual Documentation: Learn about Schema.org’s standardized definitions for multilingual content
2. Google’s Structured Data Multilingual Guide: Google’s officially recommended practices for multilingual structured data
3. JSON-LD Official Syntax Specification: Master JSON-LD’s native syntax and the usage of the @language keyword
4. Google Rich Results Test: Verify whether multilingual JSON-LD is correctly identified