What is a Substitute Query?
Definition:
A Substitute Query is a query that can take the place of another query without altering its context.
Purpose: They are used for emphasizing (or “bolding”) certain parts of content, making the context more important.
Key Point:
Unlike simple synonyms, Substitute Queries are chosen because they can replace words without changing the intended meaning of the query.
Example:
Context of Repair:
“car repair” and “auto repair” can both work because car and auto are interchangeable when talking about repairs.
Context of Railroad:
You might say “railroad car” (which makes sense) but not “railroad auto” (which would change the context).
🚂 Railroad Example: There is a railroad car, but auto doesn’t fit in this context.
Note: Substitute Queries are not just synonyms. They are carefully chosen replacements that preserve context exactly.
2. Why are Substitute Queries Important? 🌟
Emphasizing Context:
Substitute Queries make the context of a search more prominent.
Synonyms might change context because they can carry different nuances.
Ensuring Accuracy:
They ensure that the replacement words do not alter the meaning. This is crucial when the subject matter is sensitive to context.
Example:
When searching for repair services, using “car” vs. “auto” may work interchangeably. However, in queries related to railroads, only “car” is appropriate because it maintains the correct meaning.
3. How are Substitute Queries Supported? 🔧
A. Co-occurrence Matrix 📊
What It Does:
A co-occurrence matrix tracks how often words appear together in a body of text.
How It Helps:
If two queries share many common, co-occurring words, they are likely to be similar in meaning.
B. Phrase-Based Indexing 📑
What It Does:
This method indexes phrases rather than just individual words.
How It Helps:
It allows the system to consider the order and grouping of words, making sure that the context is preserved when replacing one query with another.
C. Space Vectors 🌌
What They Are:
Space Vectors represent words as vectors (numerical representations) in a multi-dimensional space.
How They Help:
By comparing these vectors, the system can determine if two queries are similar enough—if their word vectors are close together, they’re considered to be in the same context.
Example:
Similar Queries for Repair:
“car repair” and “auto repair” might share many co-occurring words (like “mechanic,” “service,” “maintenance”) and have similar vector representations.
Thus, the system can confidently substitute one for the other without changing the context.
What Does Google Do With These Queries?
Refining and Expanding:
Google uses the revised queries to search its vast index more effectively, ensuring you get the most relevant and useful results.
Personalizing Results:
By understanding the true intent behind your query—whether through canonicalization or substitution—Google can tailor results to your needs.
Quality Assurance:
The refined queries help in filtering out irrelevant content and reducing ambiguity, which means less frustration and more accurate information for you!
Evaluating Substitute Terms Using Vectors
1. Selecting the Terms (Step 310) 🎯
What Happens?
The system selects:
- A first term: The original query term.
- A candidate substitute term: A potential replacement for the first term.
Where Do They Come From?
They might be part of an existing substitution rule under evaluation.
Or, they could come from a “break and join” process that:
- Splits a term into parts, or
- Joins a multi-term phrase into one term.
Example:
The terms "French open" (two words) and "Frenchopen" (one word) can be candidates for substitution.
Note: If a substitution rule already has a high confidence (e.g., “run” → “runs”), the system might skip further vector evaluation.
2. Generating the First Vector (Steps 320 & 330) 🚀
Step 320: Determine Co-Occurrence Frequencies for the First Term
What It Means:
The system calculates how often other terms appear together with the first term in search queries.
How Is It Computed?
Count the number of times each term co-occurs with the first term.
Divide by the number of queries containing the first term (or by the total number of queries).
Example:
For the term "star", if many queries include “star wars,” the system counts how often “wars” appears with “star.”
If “wars” is too common (like in “star wars”), it might later be filtered out.
Step 330: Generate the First Vector
What It Does:
The system creates a vector where each element represents a co-occurrence frequency for a particular term that appears with the first term.
Extra Detail:
The vector might also include frequencies for terms that only co-occur with the candidate substitute term (for cross-reference).
Visual Snapshot:
Imagine the vector as a list of numbers:
First Term Vector: [Frequency of "wars", Frequency of "movie", Frequency of "space", …]
3. Generating the Second Vector (Steps 340 & 350) 🔢
Step 340: Determine Co-Occurrence Frequencies for the Candidate Substitute Term
What It Means:
Similar to Step 320, but now the system focuses on the candidate substitute term.
Example:
If evaluating "Frenchopen", the system counts how often other terms occur with "Frenchopen" in queries.
Step 350: Generate the Second Vector
What It Does:
Creates a vector for the candidate substitute term using its co-occurrence frequencies.
Visual Snapshot:
Candidate Substitute Term Vector: [Frequency of "tournament", Frequency of "tennis", Frequency of "Grand Slam", …]
Note:
The second vector might also include frequency values for terms that only appeared with the first term.
4. Comparing the Two Vectors (Step 360) ⚖️
Objective:
Compare the first and second vectors to see how similar the term contexts are.
How?
Vector Similarity Measures are used. A popular choice is cosine similarity.
Cosine Similarity Formula:
similarity = (∑i=1n Ai × Bi) / (√(∑i=1n Ai²) × √(∑i=1n Bi²))
A and B are the two vectors (each of length n).
The formula calculates the cosine of the angle between the two vectors—a higher value means more similarity.
Example:
If the vector for "French open" is very similar to the vector for "Frenchopen", the cosine similarity will be high, indicating that they occur in similar query contexts.
5. Scoring the Association (Step 370) 📈✅
What Happens?
The system uses the computed similarity measure to score the confidence in the substitution rule.
How the Score Affects the Rule:
High Similarity:
If the similarity measure meets or exceeds a certain threshold, the confidence score for the substitution rule increases.
Low Similarity:
If it falls below the threshold, the system may lower the confidence score or eliminate the substitution rule.
Example:
For the term "run" and its candidate substitute "runs":
If the cosine similarity is high, the rule is reinforced.
If not, the system may decide that “runs” is not a good substitute in that context.