Natural Language Query Parsing with Universal Knowledge Frames
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Solution Overview
Problem
Conventional methods for semantic parsing of natural language queries in databases require extensive retraining to incorporate updated background knowledge, leading to significant model training overhead and limitations in extracting all necessary knowledge from training datasets.
Innovation Solution
Representing various kinds of knowledge from different fields into universal structured knowledge representations, allowing models to parse new fields without retraining by using a universal knowledge frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used for semantic parsing, then models can be trained to understand domain-specific knowledge, but extensive retraining is required when updated background knowledge is needed, leading to significant model training overhead
Solution Approach 1:
The patent segments knowledge into two parts: (1) a universal knowledge frame that remains static and contains general parsing rules, and (2) domain-specific knowledge representations that can be dynamically added. This segmentation allows the model to maintain reliable parsing accuracy while avoiding retraining of the universal component when new domain knowledge is needed.
Solution Approach 2:
The universal knowledge frame serves multiple functions: it provides the base parsing framework, handles general knowledge representation, and acts as a foundation for domain-specific extensions. This universality eliminates the need for complete model retraining when adapting to new domains, as the universal component remains applicable across all domains.
2Loss of information
If conventional methods are used, then models can extract knowledge from training datasets, but they cannot extract all necessary knowledge and require extensive retraining for updated background knowledge
Solution Approach 1:
The universal knowledge frame is prepared in advance with general parsing rules and knowledge representation frameworks. When new domain knowledge is needed, only the domain-specific representations need to be added to this pre-prepared framework, rather than retraining the entire model. This preliminary action reduces both training complexity and knowledge extraction completeness issues.
3Adaptability or versatility
If domain-specific knowledge is incorporated into the model, then understanding of natural language queries improves, but the model requires extensive retraining for each new domain
Solution Approach 1:
The model is segmented into a universal knowledge frame (unchanging) and domain-specific knowledge representations (changeable). This allows the model to adapt to new domains by simply adding domain-specific representations without retraining the universal component, thus improving adaptability while reducing training effort.
Solution Approach 2:
The universal knowledge frame is designed to be multi-functional, serving as the base for all domain-specific extensions. This universality enables the model to handle multiple domains efficiently by leveraging the same universal parsing framework, reducing the training effort required for each new domain while maintaining high adaptability.
Data Source
AI summary
According to implementations of the subject matter described herein, a solution for semantic parsing of the natural language query is provided. In this solution, a first natural language query for a first data table is received, where the first natural language query comprises words. A first set of knowledge representations associated with the first data table are obtained and the first set of knowledge representations describe, in a structured manner, knowledge required for understanding a natural language query for the first data table. The first natural language query is converted into a first sequence of symbols. A first computer-executable query corresponding to the first natural language query is generated. According to this solution, various kinds of knowledge representations from different fields can be represented into universal structured knowledge representations, such that the knowledge representations are used to assist in understanding semantics of natural language queries and generating corresponding computer-executable queries.