What is Entity Seeking Query?

An "Entity Seeking Query" refers to a search query where the primary goal is to identify a specific entity—such as a person, place, organization, or concept—rather than just retrieving general information. Essentially, the user wants to pinpoint a particular "thing" within a broader category based on the context of the query.

🔑 Key Points About Entity Seeking Queries:

• ✅ Focus on Specific Entities:
  Unlike general search queries, an entity-seeking query is designed to find a unique and well-defined entity rather than broad information.
• ✅ Context is Crucial:
  To accurately identify the intended entity, search engines must understand the context of the query and the relationships between different entities.

📌 Example Queries:

• ❓ "What is the hotel that looks like a sail?" → Burj Al Arab Jumeirah 🏨
• ❓ "Who is the CEO of Tesla?" → Elon Musk 🚀

Process of Processing Entity Seeking Queries:

What It Means: The entire process is managed by different components of the system working together:

• Query Mapper (🗺️): Converts the query into a semantic dependency tree.
• Entity Type Identifier (🔎): Determines the type of entity you are searching for (e.g., “Chinese restaurant”).
• Entity Identifier (🏷️): Searches various data stores and calculates relevance scores to pick the correct entity.
• Subquery Resolver (🔄): Handles parts of the query separately and replaces them with their answers.
• Query Responder (💬): Provides the final response to your query.

Example Flow Recap:

• User Query: “Call the Chinese restaurant on Piccadilly Street 15.”
• Step 1: Query Mapper creates a dependency tree.
• Step 2: Entity Type Identifier determines the entity type is “Chinese restaurant.”
• Step 3: Entity Identifier searches databases, finds “China Gourmet” with a sufficient relevance score.
• Step 4: If needed, Subquery Resolver clarifies ambiguous parts.
• Step 5: Query Responder returns “China Gourmet” as the answer.

1️⃣ Overview: Two Kinds of Queries

The system handles two types of queries:

• Entity-Seeking Queries: These ask for a specific object, place, or item.
• Non-Entity-Seeking Queries: These ask for information or data (like the weather).

Examples:

• Entity-Seeking:
  • Query: “What is the hotel that looks like a sail?”
  • Action: The system finds that the Burj Al Arab Jumeirah is the hotel 🏨 in question and responds with, “The Burj Al Arab Jumeirah.”
• Non-Entity-Seeking:
  • Query: “What is the weather ☀️ today?”
  • Action: The system resolves this by providing weather details (e.g., “The weather will be between 60-70° F and sunny today.”)

2️⃣ Actions for Queries That Seek Entities

When a query is determined to be entity-seeking, the system can perform several actions:

A. Identifying Entity Types and Quantity

What It Does: Determines what type of entity the query is asking about (e.g., hotel, restaurant, movie). Figures out if the query is asking for a single entity or multiple entities.

Examples:

• Single Entity: “What is the hotel that looks like a sail?” is interpreted as looking for one hotel.
• Multiple Entities: “What restaurants nearby serve omelets?” could return several restaurants.

B. Finding the Most Relevant Entity(ies)

What It Does: Searches various databases (web resources, user history, location data, etc.) to find the entity that best matches the query. It then checks if the match is sufficiently relevant before providing a response.

Example: After analyzing “looks like a sail,” the system confirms that Burj Al Arab Jumeirah is a strong match and then audibly says, “Burj Al Arab Jumeirah.”

C. Initiating a Dialog for More Details

What It Does: If the system finds multiple possible matches, it may ask the user for extra details. The additional input is combined with the original query to refine the search.

Example:

• Initial Query: “Find a restaurant near me.”
• System Response: “Can you give me more details?”
• User Provides: “Something gourmet.”
• Result: The system now better identifies the intended restaurant.

D. Handling Subqueries

What It Does: Breaks a complex query into smaller parts (subqueries) that are each entity-seeking. Answers these subqueries individually and then plugs the answers back into the original query.

Example:

• Original Query: “Call the hotel that looks like a sail.”
• Subquery Identified: “the hotel that looks like a sail”
• Subquery Answer: “Burj Al Arab Jumeirah”
• Modified Query: “Call the Burj Al Arab Jumeirah”
• The system then executes this resolved query.

E. Adapting Based on Recent User Activity

What It Does: Monitors the types of queries a user submits in a short time span. If, for example, 60% of recent queries were entity-seeking, the system adjusts its processing for the next query accordingly.

Example: After several queries about hotels, restaurants, or movies, the system “learns” that you’re currently interested in entities and fine-tunes its search approach.

Emoji Highlights:

• 📊 Behavior Analysis
• ⚙️ Adaptive Processing

3️⃣ Advantages of the System

A. Faster Query Resolution

Benefit: Provides immediate, precise answers.

Example: When asked “What hotel looks like a sail?”, it quickly returns “Burj Al Arab Jumeirah” rather than showing a list or “no results found.”

Emoji Highlights: ⚡ Speed, 😊 User Satisfaction

B. Efficient Entity Identification

Benefit: Limits the search to only the relevant type of entity.

Example: If the query is about a hotel, the system searches only among hotels, not among all possible entity types.

4️⃣ Privacy and User Control

Data Protection Measures: Personally identifiable information is removed or generalized.
Example: Your precise location might be generalized to just the city or ZIP code.

5️⃣ Mapping a Query to a Semantic Dependency Tree

The system uses a semantic dependency tree to understand the structure and meaning of your query.

A. What Is a Semantic Dependency Tree?

Definition: A tree-like structure where each node represents one or more terms from your query. Directed edges show how terms modify one another.

Example: Query: “Call the Chinese restaurant on Piccadilly Street 15.”

Tree Structure:

• Root Node: “Chinese restaurant” (main subject)
• Child Nodes: “Call” (action) and “Street”
• Further Children: “Piccadilly” and “Fifteen” (modifiers of “Street”)

B. Determining the Entity Type from the Tree

How It Works: The system analyzes the root (and sometimes other nodes) to decide which entity type is being sought.

Example 1: Query: “Call the Chinese restaurant on Piccadilly Street 15.”
Analysis: The root “Chinese restaurant” directly indicates the entity type.

Example 2: Query: “Play the theme song from the Titanic.”
Analysis: The root might not directly say “song,” but a child node (“the theme song”) clarifies that the entity type is song.

Nuances: The system checks if the term is singular or plural to decide if you’re asking for one or multiple entities.

6️⃣ Identifying a Specific Entity Based on Relevance

A. Using Relevance Thresholds and Scores

How It Works: A relevance threshold (like 70% for a “Chinese restaurant”) is set. The system calculates a relevance score for each candidate entity. If an entity’s score meets or exceeds the threshold, it is selected.

Example: Candidate: “China Gourmet” scores 75% for the query “Call the Chinese restaurant on Piccadilly Street 15.”
Decision: Since 75% ≥ 70%, “China Gourmet” is chosen.

B. Handling Multiple Relevant Entities

Scenario: If two entities (e.g., “China Gourmet” at 75% and “Panda Cafe” at 70%) both meet the threshold but the query is meant to be singular, the system might ask for more details.

Example:

• System Prompt: “Please provide more details.”
• User Input: “Something gourmet.”
• Result: The system then selects only the best match (e.g., “China Gourmet”).

8️⃣ Additional Implementation Details

A. Adaptive Query Modes

What It Does: If a certain percentage of recent queries are entity-seeking, the system may switch to an “answer-seeking mode” that prioritizes entity identification.

Emoji Highlights: ⚙️ Adaptive Mode

B. Dependency Tree Variants

The dependency tree can be semantic (focusing on meaning) or syntactic (focusing on grammatical structure), depending on the implementation.

9️⃣ Gathering Data from Multiple Sources

What It Means: The system doesn’t rely on just one database. It collects and cross-references information from several data stores to determine the best answer.

Data Stores Used:

• Web Resources: The system sends your query to a web search engine, gathers search results, and extracts entities from those results.
  Example: Finding details about “China Gourmet” from restaurant review sites.
• User Location: It checks your location history to see if you’ve been near any relevant entities.
  Example: If you often visit a specific Chinese restaurant, that might influence the answer.
• User Activity: Past interactions (like movies watched or restaurants visited) help fine-tune the results.
• Geographical Data: Local search results are used to find nearby entities that match your query.