Natural Language Query Generation via ML Classifier
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Solution Overview
Problem
Existing search systems require predefined formats and specialized interfaces for querying datasets, limiting natural language searches and inefficiently translating words into query language, often failing to accurately identify search concepts and requiring extensive revisions.
Innovation Solution
A method involving identifying words in natural language requests, associating them with part-of-speech identifiers, grouping these identifiers, and using a machine learning classifier to generate structured queries that accurately map words to appropriate query clauses, allowing for complex natural language searches without predefined vocabulary constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If word-by-word translation approach is used to convert natural language to query language, then the translation process is simple, but the accuracy of identifying search concepts deteriorates
Solution Approach 1:
The patent segments the natural language query into individual words and phrases, then processes each segment through part-of-speech tagging and grouping operations. This segmentation allows the system to analyze the structural relationships between different components of the query rather than treating it as a monolithic string, thereby improving concept identification accuracy while maintaining manageable processing complexity.
Solution Approach 2:
The patent introduces an intermediary machine learning classifier that acts as a mediator between the raw natural language input and the final structured query. This classifier receives part-of-speech tagged words and their groupings, then predicts the appropriate query structure and clause assignments. This intermediary layer bridges the gap between simple word translation and accurate concept identification without requiring excessive complexity in either the input processing or output generation stages.
2Stability of the object's composition
If predefined format and specialized interface are required for querying datasets, then the query structure is standardized, but the ease of operation deteriorates
Solution Approach 1:
The patent inverts the traditional query approach by allowing users to input natural language freely without following predefined formats, then automatically generating the standardized query structure. Instead of requiring users to adapt their input to the system's expected format, the system adapts its output to match the user's natural language expression, thereby improving ease of operation while maintaining query structure standardization through automatic structure prediction.
Solution Approach 2:
The system performs self-service by automatically analyzing the semantic structure of natural language queries, generating appropriate query clauses, and mapping them to the target dataset schema without requiring user intervention or knowledge of specialized query languages. The machine learning classifier autonomously determines the correct query structure and assigns words to appropriate clauses, eliminating the need for users to learn specialized interfaces while ensuring standardized query output.
3Measurement precision
If extensive training data is used to train the machine learning classifier, then the query generation accuracy is improved, but the loss of time and computing resources increases
Solution Approach 1:
The patent performs preliminary action by pre-processing the natural language input through part-of-speech tagging and word grouping operations before feeding it to the machine learning classifier. This preliminary structuring of the input data reduces the complexity of the classification task, allowing the model to achieve high query generation accuracy with less training data and reduced training time, as the classifier only needs to learn the mapping from structured linguistic features to query structures rather than learning from raw text.
4Measurement precision
If complex search tools and specialized query languages are used, then the measurement precision of analytics queries is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent creates a simplified copy or representation of the complex query language through natural language processing. Instead of requiring users to directly manipulate complex query syntax, the system translates natural language expressions into the appropriate query structure, effectively creating a user-friendly interface layer that copies the functionality of specialized query tools without their complexity. This allows business users to perform accurate analytics queries using simple natural language while the system handles the complex translation to precise query syntax.
Data Source
AI summary
Techniques for generating a query statement to query a dataset may be provided. For example, the query statement can be generated from natural language input, such as a natural language utterance. To do so, the input can be analyzed to detect a sentence, identify words in the sentence, and tag the words with the corresponding word types (e.g., nouns, verbs, adjectives, etc.). Expressions using the tags can be generated. Data about the expressions can be inputted to a classifier. Based on a detected pattern associated with the expressions, the classifier can predict a structure of the query statement, such as what expressions correspond to what clauses of the query statement. Based on this prediction, words associated with the expressions can be added to the clauses to generate the query statement and accordingly query the dataset.