Search Query Processing via N-gram Entity Mapping
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Solution Overview
Problem
Existing search systems face computational intensity when matching search queries with grammar rules, as they often represent rules as trees and traverse them extensively, requiring significant computing resources and time.
Innovation Solution
A search server that splits search terms into tokens, generates n-grams, and maps entity types, intent words, and modifier words within the search query to identify matching grammar rules by querying a keyword data store and a grammar data store, reducing the computational burden through efficient data mapping and traversal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If grammar rules are represented as trees and traversed to determine query matching, then comprehensive grammar rule matching is achieved, but computing resources and processing time increase significantly
Solution Approach 1:
The patent segments the grammar rule matching process into distinct phases: generating tokens from search terms, creating n-grams from tokens, querying the keyword data store with n-grams to identify entity types, and then matching entity types against grammar rules. This segmentation eliminates the need to traverse entire grammar trees, significantly reducing computational complexity while maintaining matching accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing search queries into tokens and n-grams before grammar rule matching. The keyword data store is pre-populated with entity types and their associated n-grams. This preliminary preparation allows for direct querying and comparison rather than exhaustive tree traversal during the actual matching phase.
2Measurement precision
If extensive tree traversal is performed to match search queries with grammar rules, then accurate matching is achieved, but computational intensity increases
Solution Approach 1:
The patent applies partial action by querying the keyword data store with only the generated n-grams to identify entity types, rather than performing complete tree traversal of all grammar rules. This partial matching approach achieves sufficient precision for search query processing while dramatically reducing the computational energy required compared to exhaustive tree traversal.
3Reliability
If grammar rules are checked comprehensively for each search query, then matching accuracy is maintained, but processing time increases
Solution Approach 1:
The patent segments the matching process into discrete steps: token generation, n-gram creation, entity type identification through data store querying, and grammar rule matching. This segmentation allows the system to process only relevant portions of grammar rules based on the extracted entity types, maintaining reliability while reducing processing time compared to comprehensive tree traversal.
Solution Approach 2:
The patent uses a data store that stores copies of entity type information and their associated n-grams. Instead of traversing grammar trees during query processing, the system queries this pre-populated data store structure, which contains copied and organized entity type data. This copying approach enables faster retrieval and matching while maintaining the integrity and reliability of grammar rule matching.
Data Source
AI summary
A search server stores a plurality of keywords, each associated with an entity type. The server stores grammar rules, each specifying one or more entity types. The server receives a search query with search terms and generates tokens by splitting the search terms, each token associated with a token position that indicates a location of the token within the query. The server generates n-grams. Each n-gram is associated with a start token position and an end token position. The server determines entity types for n-grams that correspond to the keywords and generates a mapping that maps the received entity types and the start token positions of the n-grams that correspond with the received entity types to the end token positions of the n-grams that correspond with the received entity types. The server identifies grammar rules based on the mapping and transmits a result based on the identified grammar rules.