Natural Language Query Parameter Mapping with Entity Disambiguation
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Solution Overview
Problem
Natural language searches are rudimentary, requiring word or phrase matching and are unintuitive, making it difficult to transform complex human utterances into query parameters for database queries.
Innovation Solution
A method that tokenizes utterances into feature vectors, assigns entity labels, and resolves them to standardized database query values using machine learning models and string-based algorithms, incorporating disambiguation engines to handle colloquialisms and synonyms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language searches use simple word or phrase matching, then the search operation is simple and fast, but the search precision and ability to understand complex human intent deteriorates
Solution Approach 1:
The system segments the natural language utterance into individual tokens, then transforms each token into a feature vector. This segmentation allows the system to process complex queries by breaking them down into manageable components that can be individually analyzed and transformed into structured query parameters.
Solution Approach 2:
The system introduces feature vectors as an intermediary representation between raw text tokens and final query parameters. These feature vectors capture semantic information and enable the disambiguation engine to resolve tokens to standardized database values, thereby improving search precision without requiring direct complex pattern matching.
2Measurement precision
If the system transforms utterances into query parameters using complex processing, then the query precision improves, but the processing time and operational complexity increases
Solution Approach 1:
The system performs preliminary transformation of tokens into feature vectors before the actual disambiguation and resolution process. This preliminary action prepares the data in a standardized format that facilitates faster and more accurate resolution to query parameters, reducing the time required for the critical disambiguation step.
Solution Approach 2:
The system changes the representation parameters of the input data by transforming text tokens into feature vectors with specific dimensional properties. This parameter transformation enables efficient processing by the disambiguation engine, as the feature vector format allows for rapid comparison and matching against reference data tables.
3Adaptability or versatility
If the system uses detailed entity labeling and disambiguation, then the ability to resolve ambiguous terms improves, but the computational complexity and processing steps increase
Solution Approach 1:
The disambiguation engine serves multiple functions: it resolves colloquialisms, handles synonyms, disambiguates entity references, and maps tokens to standardized database values. This multi-functional approach enables the system to handle diverse language variations through a single unified processing component, improving adaptability without proportionally increasing overall system complexity.
Data Source
AI summary
A method may receive a text string of an utterance. A method may tokenize the utterance into a plurality of tokens. A method may transform the plurality of tokens into a plurality of feature vectors. A method may assign an entity label to each of the plurality of feature vectors. A method may resolve each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language.