Ontological Model for Natural Language Query to SQL Conversion
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Solution Overview
Problem
Conventional systems that utilize arbitrary text queries fail to efficiently and accurately generate reports from data retrieved from multiple data sources due to their inability to consider the semantics and meaning of user queries, requiring specialized training and knowledge, and are overwhelmed by voluminous requests.
Innovation Solution
A system that employs ontological models to generate semantic queries from natural language queries, converting them into SQL queries to retrieve and analyze data from multiple data sources, allowing users to query data without needing specific training, and providing accurate and timely responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use arbitrary text queries to retrieve data, then the system structure remains simple, but the system cannot understand the semantics and meaning of user queries, leading to inaccurate report generation
Solution Approach 1:
The patent introduces an ontological model as an intermediary layer between natural language queries and SQL query generation. This model includes entities, attributes, and relationships that bridge the gap between human language and database queries, enabling semantic understanding without requiring the system to be overly complex. The ontological model acts as a mediator that translates semantic concepts into structured queries.
Solution Approach 2:
The system performs preliminary action by pre-defining ontological models that capture the semantic structure of the domain before query processing. These models include predefined entities, attributes, and relationships that are established in advance, allowing the system to quickly match user queries against known semantic patterns rather than building understanding from scratch for each query.
2Productivity
If conventional systems process voluminous requests without semantic understanding, then the system can handle high request volume, but the response accuracy and relevance deteriorate
Solution Approach 1:
The system pre-defines ontological models that capture the semantic structure of the domain before query processing. These models include predefined entities, attributes, and relationships that are established in advance, allowing the system to quickly match user queries against known semantic patterns rather than building understanding from scratch for each query.
Solution Approach 2:
The patent segments the query processing task into distinct components: natural language understanding, semantic matching against ontological models, and SQL query generation. This segmentation allows each component to be optimized independently, maintaining high processing capacity while improving accuracy through specialized semantic matching.
3Ease of operation
If conventional systems require specialized training for operators to query data effectively, then query accuracy may improve, but the ease of operation deteriorates
Solution Approach 1:
The ontological model serves as an intermediary that handles the complexity of semantic understanding and query formulation. Users interact with the system using natural language without needing to understand the underlying complex query structures or database schemas. The ontological model mediates between simple user input and complex query generation, making the system easy to operate while maintaining reliability.
Solution Approach 2:
The system performs self-service by automatically generating appropriate SQL queries from natural language input without requiring user expertise in query formulation. The ontological model enables the system to autonomously understand user intent and translate it into effective queries, eliminating the need for specialized operator training.
4Measurement precision
If conventional systems generate reports without semantic understanding, then processing speed may be maintained, but the relevance and accuracy of generated reports deteriorate
Solution Approach 1:
The system pre-defines ontological models that capture the semantic structure of the domain before query processing. These models include predefined entities, attributes, and relationships that are established in advance, allowing the system to quickly match user queries against known semantic patterns rather than building understanding from scratch for each query.
Solution Approach 2:
The patent segments the query processing task into distinct components: natural language understanding, semantic matching against ontological models, and SQL query generation. This segmentation allows each component to be optimized independently, maintaining high processing capacity while improving accuracy through specialized semantic matching.
Data Source
AI summary
Technologies for enabling retrieval of stored information in response to natural language queries include an information retrieval server. The information retrieval server receives a request for information from a user in the form of a natural language query. The received natural language query is compared to various previously-generated semantic queries, each of which is associated with a Structured Query Language query. Based on the comparison, the information retrieval server determines the previously-generated semantic query that most closely matches the received natural language query. Thereafter, the Structured Query Language query associated with the determined semantic query is identified. The information retrieval server determines executes the identified Structured Query Language query to retrieve the information requested by the user. The retrieved information is presented to the user by the information retrieval server. Other embodiments are described and claimed.