🌱 Seed Queries: A Detailed Guide

Seed queries are essential components in search and data retrieval systems. They act as the starting point for generating a variety of related search queries and understanding user intent.

1. What Are Seed Queries? 🤔

Seed queries come in two main forms:

• Synthetic Queries:
  These are generated by machines or algorithms based on certain rules or data.
  Example: A system automatically creates the query "Top 10 summer vacation spots" based on trending travel data.
• User-Generated Queries:
  These originate from real users when they type in search terms.
  Example: A user types "best Italian restaurants near me" into a search engine.

Key Point: Seed Query Necessity: The query must return a satisfying set of documents (i.e., the search results should be relevant and helpful).
Example:
If "healthy smoothie recipes" returns high-quality, varied recipes that users enjoy, it qualifies as a seed query.

2. Characteristics of a Good Seed Query 🌟

A query is marked as a seed query if it meets these three essential criteria:

• Logical:
  The query makes sense in its context.
  Example:
  Logical: "best workout routines for beginners" is clear and understandable.
  Illogical: "banana car drive" would be confusing and irrelevant.
• Popular:
  Many users are interested in or use the query, indicating its relevance.
  Example:
  Popular: "latest smartphone reviews" is frequently searched because many people want up-to-date tech information.
• Satisfying:
  The search results provide valuable and accurate information to the user.
  Example:
  Satisfying: "easy vegan dinner recipes" returns a variety of well-rated recipes that users appreciate.

Note: Whether a query is synthetic or user-generated, if it is logical, popular, and satisfying, it will be marked as a seed query.

3. Why Use Seed Queries? 🔑

Seed queries serve several important purposes in the world of search engines and data analysis:

• Determining Representative Queries:
  They help in identifying a baseline or representative query from which variations can be derived.
  Example:
  Seed Query: "budget travel tips"
  Variations Generated: "affordable travel advice", "cheap vacation ideas", "low-cost travel hacks"
• Creating Query Variations:
  They act as templates to generate multiple related queries, ensuring a broader and more precise search coverage.
  Example:
  Seed Query: "how to train a dog"
  Query Variations:
  "dog training basics", "tips for training puppies", "best dog training techniques"
• Developing Intent Templates:
  They help in understanding the underlying intent of user searches, which in turn aids in categorizing and tailoring search results.
  Example:
  Seed Query: "healthy dessert recipes"
  Intent Template: Users are looking for dessert recipes that are both nutritious and delicious, which can then be mapped to other similar queries like "low-calorie dessert ideas".

Canonicalizing Search Queries to Natural Language Questions

This patent explains processing of natural language search queries to ensure they are well-formed (i.e., grammatically correct, clear, and error-free). If a query isn’t well-formed, a trained canonicalization model can transform it into a better version.

• Receive Query:
  User submits a natural language query from their device.
• Determine Well-Formedness:
  Use the classification model to analyze the query’s structure and grammar.
• Generate Variant (if needed):
  If well-formed: Proceed with the original query.
  If not well-formed: Use the canonicalization model to create a better version.
• Handle Related Queries:
  Evaluate and possibly canonicalize related queries for clearer user intent.
• Deliver Results:
  Send the (original or improved) query to the search system, and display the search results on the client device.

1. What Does “Well-Formed” Mean? ✅

A well-formed query adheres to the grammar rules of a language and is easy for both humans and machines to understand. In many cases, it is:

• Grammatically correct
• Free of spelling errors
• Explicit in its intent (often phrased as a clear question)

Example:
Not Well-Formed: "Hypothetical Café directions"
Well-Formed: "What are directions to Hypothetical Café?"

Step 1: Receiving and Analyzing the Query 📥

When a user submits a natural language query from their device:

• Input: The search query is captured as text.
• Objective: Determine whether this query is well-formed.
• Key Points:
  The system examines features such as characters, words, parts of speech, and more. It uses linguistic representations like word n-grams (groups of words) and character n-grams.
• Example: A user types "reset hypothetical router". The system will analyze its structure and grammar.

Step 2: Determining Well-Formedness Using a Classification Model 🤖

The system uses a trained classification model (often a neural network) to decide if the query is well-formed.

• Feature Extraction: The model takes various features from the query (e.g., individual words, their sequence, parts of speech) as input.
• Processing Layers: These features pass through multiple feed-forward layers (think of these as decision-making steps) and finally a softmax layer that outputs a probability.
• Output: A value (usually between 0 and 1) indicates how well-formed the query is.
• Example: For the query "reset hypothetical router", the classification model might output a probability of 0.3 (on a scale where a higher number means more well-formed), suggesting it needs improvement.

Step 3: Generating a Well-Formed Variant with a Canonicalization Model 🔄

If the classification model determines that a query is not well-formed, the system uses a trained canonicalization model to generate an improved version.

• Canonicalization Model: Often a sequence-to-sequence model based on a recurrent neural network (RNN), which might include memory layers such as LSTM or GRU units.
• Encoder-Decoder Structure:
  Encoder: Processes the input query and converts it into a meaningful internal representation (encoding).
  Decoder: Transforms the encoding into a well-formed query.
• Example: For the problematic query "reset hypothetical router", the canonicalization model might generate the well-formed variant:
  "How do I reset a hypothetical router?"

Efficiency: Only Fix What Needs Fixing ⚡

The system is designed to conserve computing resources:

• If a Query is Already Well-Formed:
  The classification model confirms its status, and the canonicalization model is not used.
  Example: A query like "What are directions to Hypothetical Café?" is used as-is.
• If a Query is Not Well-Formed:
  The canonicalization model generates the improved version.

6. Handling Related Queries 🔗

In addition to processing the primary search query, the system can also manage related queries:

• Identify Related Queries: These are queries often submitted alongside the primary query.
• Check Their Well-Formedness: The same classification model evaluates if these related queries are well-formed.
• Canonicalize if Needed: If a related query isn’t well-formed, a variant is generated.
  Example:
  Related Query (Not Well-Formed): "reset hypothetical router"
  Canonicalized Variant: "How do I reset a hypothetical router?"
• Association: This well-formed variant can be stored and later presented as a selectable link. When the user selects it, the system processes the improved query to fetch more accurate results.