Search Engine Query Completion via Structured Data Tuples
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Solution Overview
Problem
Existing search engines' suggested queries are limited by past user submissions, failing to leverage structured data schemas and leading to a diminished search experience, especially when users seek new information or products.
Innovation Solution
A search engine system that generates query completions based on attribute combinations and metadata associated with structured data tuples, including synonyms and misspellings, allowing users to interact and refine queries without knowing the underlying data schema, and providing dynamic query suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If query suggestions are based on past user submissions, then the system can provide suggestions using existing data, but the suggestions are limited to previously submitted queries and cannot reflect new products or topics
Solution Approach 1:
The system pre-generates query suggestions from structured data schemas before users submit queries. By organizing data attributes and values into potential query patterns in advance, the system can immediately suggest relevant queries for new products or topics without waiting for past user submissions, thus improving both accuracy and coverage
Solution Approach 2:
The patent introduces structured data schemas as an intermediary between raw data and user queries. The schemas act as a bridge that transforms unstructured or semi-structured data into organized attribute-value pairs, enabling the generation of accurate query suggestions while adapting to new information domains through schema evolution
2Adaptability or versatility
If query suggestions rely on unstructured or semi-structured data, then the system can handle diverse data sources, but the suggestions cannot take advantage of defined schemas and attribute relationships
Solution Approach 1:
The system transforms data from various structural formats into a standardized attribute-value representation. By changing the parameter structure of raw data into organized schemas with defined attributes, relationships, and constraints, the system enables precise query suggestion generation while maintaining flexibility to process diverse data sources through schema adaptation
3Measurement precision
If the system exposes detailed data schemas to users, then query suggestions can be highly accurate, but users must understand complex underlying data structures
Solution Approach 1:
The patent uses natural language processing and synonym expansion as intermediaries between structured data schemas and user interfaces. The system translates formal attribute names and values into natural language queries that users can understand and interact with, while maintaining the precision of schema-based suggestions. This mediator layer allows users to benefit from structured data accuracy without needing to understand complex data models
Solution Approach 2:
The system creates simplified copies or representations of the underlying data schemas for user interaction. Instead of exposing the actual complex schema structure, the system generates user-friendly query templates and suggestions that mirror the schema's organizational logic in an accessible format, allowing users to interact with simplified versions that preserve accuracy while improving ease of use
Data Source
AI summary
Data, such as product data or airline flight data, is represented using structured data tuples, tables, or as data with related metadata and tags, and stored by a search engine. Partial queries are received by the search engine from a user and are used to generate a dialog between the search engine and the user. The dialog may include suggested query completions for the partial queries that correspond to a schema associated with the data tuples. The suggested query completions may be determined using attribute combinations of attributes and attribute values, or metadata and tags associated with the data tuples, including known synonyms and misspellings. The user may interact with the query completions in the dialog, and the search engine may revise the dialog and the query completions according to the interactions. A user may query data tuples without knowing the schema used by the underlying data structures.