Facet-Based Conversational Search Query Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional database search tools require users to manually select and manage numerous facets, making it difficult to formulate effective search queries, especially in large datasets, and often result in inaccurate or irrelevant search results due to the complexity of managing multiple data types and attributes.
Innovation Solution
A conversational search server that converts natural-language search queries into database queries by identifying and refining facets through linguistic models, action tagging, and coherence scoring, allowing users to issue queries in a user-friendly format and adapt them incrementally, while inferring and predicting facets based on search history and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually select and manage facets in conventional database search tools, then search coverage can be comprehensive, but user effort and complexity increase significantly
Solution Approach 1:
The system performs facet identification and query refinement automatically without requiring user intervention. The server extracts facets from natural language queries, infers missing facets, and generates refined database queries autonomously, allowing the search system to serve itself rather than requiring manual facet management from users
Solution Approach 2:
The conversational search server acts as an intermediary between the user's natural language query and the database search system. It translates user intent into structured database queries by automatically identifying and managing facets, thereby shielding users from facet complexity while maintaining comprehensive search coverage
2Measurement precision
If users manually manage multiple facets in large datasets, then search coverage can be comprehensive, but device complexity and management difficulty increase
Solution Approach 1:
The server autonomously performs facet extraction, identification, and query refinement without requiring user management of facets. It automatically processes the complexity of multiple data types and attributes by handling facet management internally through linguistic models and coherence scoring mechanisms
Solution Approach 2:
The system extracts and isolates facet management functions from the user interface, concentrating them in the conversational search server. This separates the complexity of facet management from the user's task of formulating search intent, allowing comprehensive search coverage without exposing users to facet management complexity
3Measurement precision
If conventional search tools present all available facets to users, then search completeness can be achieved, but ease of operation decreases due to information overload
Solution Approach 1:
The system extracts only the relevant facets needed for the current search query from the complete set of available facets. By identifying and extracting pertinent facets based on the natural language query and context, it presents a simplified view to users while maintaining access to comprehensive search capabilities
Solution Approach 2:
Instead of presenting all facets uniformly, the system applies local quality by tailoring the facet presentation to the specific search context. It identifies and highlights only the facets relevant to the current query and user intent, making the interface adaptive to local search needs rather than presenting global facet lists
4Ease of operation
If the system automatically infers and predicts facets, then user effort is reduced, but loss of information may occur if intent is misinterpreted
Solution Approach 1:
The system employs coherence scoring to evaluate and refine inferred facets, using feedback loops to verify that automatic facet identification accurately captures user intent. The coherence model assesses whether inferred facets are consistent with the query context and adjusts interpretations accordingly, reducing information loss from misinterpretation
Solution Approach 2:
The system performs preliminary facet inference and coherence checking before finalizing the database query. By anticipating potential interpretation errors and validating inferred facets against coherence criteria in advance, it prevents information loss while maintaining ease of operation through automatic facet management
Data Source
AI summary
A method is provided. The method comprises receiving, from a user device, a current search query that is in an unstructured format; after receiving the current search query from the user device, retrieving a corresponding current list of facets with a current list of associated values and a context of the current search query, the context comprising a previous search query from the user device and a corresponding previous list of facets with a previous list of associated values; determining a group of actions on one or more of the current list of facets with the current list of associated values and the previous list of facets with the previous list of associated values based on the current search query; creating an updated list of facets with an updated list of associated values based on the group of actions; generating a database query based on the updated list of facets with the updated list of associated values; causing a database search with the database query; transmitting a search result of the database search to the user device, wherein the method is performed by one or more computing devices.