✓ TestedWorks with CSV, Excel, Google Sheets → Converted Format
By dataclean.to team · 2026-02-13
JSON-LD (JavaScript Object Notation for Linked Data) is the format Google recommends for implementing structured data on websites. Converting tabular CSV data into JSON-LD involves more than just reformatting — it requires understanding data types, validating values, nesting related properties, and ensuring every field meets schema.org's specifications. dataclean.to automates this entire pipeline: import your CSV, clean the data, map columns to schema properties, and export valid JSON-LD for any of 19+ supported schema types.
The Problem
CSV is a flat, untyped format. JSON-LD is a hierarchical, typed format. Bridging between them requires solving multiple problems simultaneously. Every CSV value is a string, but JSON-LD properties expect specific types: integers for counts, floats for prices, ISO dates for timestamps, and absolute URLs for links. Nested JSON-LD structures (an Event's location containing a Place containing a PostalAddress) have no equivalent in flat CSV rows. Manual conversion is feasible for a handful of records but becomes impractical at scale, especially when the source data contains inconsistencies that produce invalid JSON output. JSON-LD 1.1 W3C specification
How to Fix It
1
Upload your CSV file
Import any CSV file into dataclean.to. The platform auto-detects delimiters, encoding, and handles CSV files with thousands of rows. Column headers are read automatically but don't need to match schema.org naming conventions.
2
Auto-detect data types
Since CSV strips type information, dataclean.to re-infers types from actual values: numbers, dates, URLs, booleans, phone numbers, email addresses, currencies, and identifiers. This detection drives both the cleaning rules and the JSON-LD type assignments.
3
Clean values for JSON-LD compatibility
Normalize all values for valid JSON-LD output: escape special characters that break JSON syntax, convert dates to ISO 8601, parse prices into numeric values, validate and encode URLs, and handle null/empty values appropriately for required versus optional properties.
4
Select schema type and map columns
Choose the schema.org type for your data and map CSV columns to schema properties. dataclean.to handles nesting automatically — mapping address columns creates a nested PostalAddress, mapping price and availability creates nested Offer objects.
5
Export JSON-LD output
Generate the JSON-LD output with correct @context, @type, and properly typed property values. Each CSV row becomes a complete JSON-LD object. Export as individual files, a combined array, or deploy through a hosted API endpoint.
Frequently Asked Questions
What is JSON-LD and why is it preferred over other structured data formats?
JSON-LD is a method of embedding linked data using JSON syntax. Google explicitly recommends JSON-LD over alternatives like Microdata and RDFa because it's injected as a script block separate from HTML content, making it easier to add, maintain, and debug without modifying page markup.
Can I generate JSON-LD for multiple schema types from one CSV?
Yes. You can create multiple schema mappings from the same CSV dataset. For example, a restaurant database CSV could generate Restaurant JSON-LD (for business listings) and Menu/MenuItem JSON-LD (for menu content) using different column mappings for each type.
How does dataclean.to handle CSV columns that should become nested JSON-LD objects?
When you map columns like street, city, state, and zip to address-related properties, dataclean.to automatically nests them as a PostalAddress object within the parent schema type. Similarly, price and availability columns become nested Offer objects. You don't need to structure the nesting manually.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.