Free Text Query Parsing Using Grammar Rules and Semantic Tokens
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Solution Overview
Problem
Users of business enterprise software face challenges in efficiently accessing relevant data from vast amounts of information without needing to learn complex search syntax or query languages, as existing solutions require time and expertise to navigate through data repositories effectively.
Innovation Solution
A system that uses keyword semantics and grammar rules to generate consistent queries, allowing users to input free text, parse it into tokens, and generate query trees that connect keyword tokens with business data, providing ranked search query suggestions based on compliance with predefined grammar rules, while also incorporating security and knowledge graph-based access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users input free text queries without training, then ease of operation improves, but search precision deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer between user input and data retrieval. Free text queries are parsed into tokens, which then undergo grammar rule validation and query tree construction before becoming executable search queries. This intermediary process transforms unstructured user input into precise search operations without requiring users to learn complex query syntax.
Solution Approach 2:
The system performs self-correction and self-validation through automated grammar checking and query tree validation. The grammar rules automatically detect and correct syntactic errors in user queries, and the system self-optimizes query construction by selecting appropriate data paths and join operations without human intervention.
2Measurement precision
If complex search syntax is required for accurate data retrieval, then search precision improves, but ease of operation deteriorates
Solution Approach 1:
The patent creates a simplified copy or representation of complex query syntax through grammar rules and query trees. Instead of requiring users to write complex SQL-like queries, the system uses a simplified token-based grammar that mirrors the essential structure of precise queries while being much easier to write and understand.
3Measurement precision
If free text parsing and grammar validation are implemented, then search precision improves, but device complexity increases
Solution Approach 1:
The query processing system is segmented into distinct modular components: tokenization module, grammar validation module, query tree construction module, and execution module. Each component handles a specific aspect of query processing, making the overall complex system manageable through clear separation of concerns and independent optimization of each module.
4Measurement precision
If query trees and grammar rules are used to ensure compliance, then search precision improves, but loss of time increases
Solution Approach 1:
Grammar rules and query tree structures are pre-compiled and validated before runtime. The system performs preliminary parsing of the grammar into executable validation logic, so that during actual query processing, the system only needs to check against pre-validated rules rather than interpreting complex grammar specifications in real-time, significantly reducing processing time.
Data Source
AI summary
A computer-implemented method for initiating, at a user interface, execution of a software application implementing a plurality of business processes associated with a plurality of business objects, and displaying, in the user interface, a query box and a plurality of visualizations depicting data corresponding to at least a portion of the plurality of business objects. The method includes receiving and parsing a free text input, generating and executing a search using a plurality of keyword search queries based on one or more keyword tokens. The method also includes applying grammar rules, generating query trees, and generating a number of search suggestions using the query trees.