Search Suggestions via Fuzzy-Entity Co-occurrence Matching
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Solution Overview
Problem
Current search engines face challenges in generating accurate search suggestions due to incomplete or misspelled queries, leading to irrelevant results, as existing methods based on query logs or other techniques are not sufficiently effective.
Innovation Solution
A method employing fuzzy-score matching and entity co-occurrence in a knowledge base, where an entity extraction module identifies entities in search queries and a fuzzy-score matching module uses a selected algorithm to search an entity co-occurrence database, suggesting complete queries by matching entities, topics, or facts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query logs are used to generate search suggestions, then suggestions can be provided based on past user behavior, but the suggestions may not be accurate enough and may not reflect the user's true intent
Solution Approach 1:
The patent introduces an entity co-occurrence knowledge base as an intermediary between the query log analysis and suggestion generation. This knowledge base stores pre-computed entity relationships and co-occurrence patterns, allowing the system to leverage both historical query data and structured entity knowledge to generate more accurate suggestions that reflect true user intent rather than just surface-level query patterns
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing entity co-occurrence relationships in the knowledge base before actual search suggestion generation. Entity extraction, relationship identification, and co-occurrence counting are performed in advance, enabling faster and more accurate suggestion generation when users submit queries without requiring complex real-time analysis
2Reliability
If fuzzy matching algorithms are applied to search queries, then misspelled queries can be corrected, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing queries through entity extraction and fuzzy matching against the entity co-occurrence knowledge base before full search execution. This allows misspelled queries to be corrected and entities to be identified in advance, improving reliability while reducing the computational burden during the actual search process
Solution Approach 2:
The patent segments the search process into distinct stages: entity extraction, fuzzy matching against entity database, co-occurrence-based suggestion generation, and final search execution. This segmentation allows fuzzy matching to be applied selectively to extract entities rather than to the entire query, reducing processing time while maintaining accuracy for identifying key entities
3Measurement precision
If entity extraction and fuzzy matching are performed for every search query, then accurate search suggestions can be generated, but the system complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary action by pre-building the entity co-occurrence knowledge base with extracted entities and their relationships before deployment. This pre-processing eliminates the need to perform complex entity extraction and relationship analysis for every search query, reducing system complexity during operation while maintaining high suggestion relevance through the pre-computed knowledge structure
Solution Approach 2:
The entity co-occurrence knowledge base serves itself by providing pre-computed entity relationships and co-occurrence patterns that automatically guide suggestion generation. Once built, the knowledge base requires minimal additional processing for each query, as the heavy lifting of entity extraction and relationship mapping has already been performed, reducing both computational overhead and system complexity
Data Source
AI summary
A method for generating search suggestions by using fuzzy-score matching and entity co-occurrence in a knowledge base is disclosed. Embodiments of the method may be employed in any search system that may include an entity extraction computer module that may perform partial entity extractions from provided search queries, a fuzzy-score matching computer module that may generate algorithms based on the type of entity extracted and perform a search against an entity co-occurrence knowledge base. The entity co-occurrence knowledge base, which may include a repository where entities may be indexed as entities to entities, entities to topics, or entities to facts among others, may return fast and accurate suggestions to the user to complete the search query. The suggestions may include alternates to the partial query provided by the user that may enhance and save time when performing searches.