What is Synthetic Query?
🔍 Definition:
A Synthetic Query is a modified or rewritten version of a user’s original search query. The search engine rewrites the query to improve search results and enhance user satisfaction.
🛠 How Does It Work?
Search engines modify queries by:
- ✅ Adding related words (e.g., synonyms, alternate spellings)
- ✅ Expanding queries to include broader or more specific terms
- ✅ Using structured data from web pages to refine the search
🔢 Key Scores Used in Synthetic Queries:
To determine the best rewritten query, search engines analyze:
- 📏 Edit Distance Score – Measures how much the query has changed.
- 🔗 Similarity Score – Compares how close the new query is to the original.
- 💰 Transformation Cost Score – Evaluates how much effort is needed to change the query.
📝 Example:
Original Query: "cheap smartphones USA"
Synthetic Query: "affordable mobile phones in the United States"
🔹 Here, the search engine replaces cheap with affordable and smartphones with mobile phones to improve results.
📌 Synthetic Queries & Open Information Extraction
🔗 Relationship Between Synthetic Queries & Open Information Extraction (OIE)
🔹 Synthetic Queries can be generated from:
- 📖 The same author
- 📰 The same journal
- 🔗 The same source
- 📅 The same time period
🔹 OIE helps in extracting facts from unstructured data and generating useful search queries.
🔄 Why Is This Important?
🔹 Before search engines understand entities (specific things like people, places, or products), they first need to understand phrases and relationships between words.
📝 Example:
📌 If an article talks about "Google's AI advancements in 2025"
🔹 A Synthetic Query could be generated like:
"Artificial Intelligence progress by Google in 2025"
This improves search results by rewording the query in a way that provides more accurate information! 🎯
📌 Synthetic Query & Query Templates
🧩 What is a Query Template?
A Query Template is a predefined format that helps generate Synthetic Queries. Think of it as a blueprint for rewriting searches! 🏗️
🔄 How It Works:
- 🔹 Query Templates act as a bridge between user queries and synthetic queries.
- 🔹 They allow search engines to predict and structure search results better.
🔍 Sources for Synthetic Queries:
- 📄 HTML Tags – Titles, headings, and metadata from web pages.
- 📊 IDF Scores – Measures how unique or important a term is in a document.
- 🔗 Similar Phrases – Searches for alternative ways to phrase the same query.
📝 Example:
📌 If a web page has:
- H1: Dorothy Parker Biography
- H2: Sylvia Plath
🔹 The search engine may generate the synthetic query: "Sylvia Plath Biography"
✔️ If the results are relevant and high-quality, this synthetic query can become a Seed Query (a commonly used search phrase).
🧠 How Google Creates Synthetic Queries (And Why You Should Care in SEO) Explanation by Koray
Ever wonder how Google shows results for things you’ve never searched before?
Take a look at this diagram. It’s based on a patent from the VP of Search Intelligence at Google—the same mind behind Gemini and Google’s AI mode.
Here’s what’s happening behind the scenes:
🔍 Structured Similarities Between Documents
When Google crawls a page like “J.D. Salinger – Catcher in the Rye”, it recognizes the structure:
[Entity] + [Attribute] — in this case, author + book.
Now, imagine a similar page: “Joseph Heller – Catch-22”. Google clusters these two pages together because their structures match. It then generates new, synthetic queries like:
- → “Joseph Heller short stories”
- → “J.D. Salinger biography”
- → “Books similar to Catcher in the Rye”
Even if no one’s searched for them yet, Google pre-builds indices based on these inferred similarities.
🌍 Why This Matters for Local SEO
If your site targets “Pool Cleaning in Florida,” Google might synthetically generate variations like:
- → “Pool Cleaning in Austin”
- → “Hot Tub Maintenance in Orlando”
- → “Local Pool Services in Texas”
If you’ve already built strong content around one region + service, Google can extend that relevance vector across new, related regions — before users even search.
📐 Content Briefs Should Cover Contextual Vectors
Programmatic SEO and content templates should aim to include all variations of entity-attribute pairs — especially with modifiers like time, place, intent, or condition.
⚠️ Page vs Segment Decision
Not every query deserves a standalone page. That’s where the “Page vs Segment” decision comes in:
- ➡️ If it’s a macro-context → Create a dedicated page
- ➡️ If it’s a micro-context → Add it as a segment inside a broader page
This ensures your topical map remains clean, but semantically connected.
🧠 Machine Learning Loves Bias
The more variations you feed it, the better it can predict unseen ones. By structuring your content network properly, you’re not just ranking for what exists — you’re ranking for what will exist.
🎯 Google’s “Search with Stateful Sessions”
Google’s synthetic query patents prove one thing:
They can’t wait for real users to generate every query. So they generate them themselves.
And just like that, you’re not just optimizing for keywords — you’re optimizing for Google’s imagination.