Ontology-Based Database Querying with Natural-Language LLM Translation
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Solution Overview
Problem
Querying graph databases can be challenging for non-technical users due to the complexity of the query language, necessitating a user-friendly interface for effective database interactions.
Innovation Solution
A system utilizing a large language model (LLM) to translate natural language user queries into appropriate database queries for graph and relational databases, enabling efficient data exploration and updates within ontology-based databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph databases are used to store and represent complex relationships, then data representation capability is improved, but query complexity increases for non-technical users
Solution Approach 1:
The patent introduces a natural language processing interface as an intermediary between users and the graph database query system. This mediator translates user-friendly natural language queries into appropriate graph database queries, eliminating the need for users to learn complex query languages while preserving the full capability of the graph database to represent complex relationships.
2Ease of operation
If a friendly interface is provided for non-technical users, then ease of operation is improved, but query precision may be compromised
Solution Approach 1:
The patent replaces the mechanical system of complex syntax-based query languages with an intelligent natural language processing system. This substitution allows users to interact using ordinary language while the system automatically generates precise graph database queries, maintaining both user-friendliness and query precision through AI-driven translation.
Data Source
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AI summary
A system is programmed to train or fine-tune a large language model (LLM) for converting a user query in natural language to database queries for accessing a set of databases where data related to an ontology is stored. The set of databases includes a graph database and stores metadata and actual data of the ontology. The system is further programed to receive a specific user query exploring links between objects in the ontology and leads to updates to the ontology. The system is programmed to then execute the LLM to obtain a set of database queries, including one or more graph queries. Furthermore, the system is programmed to submit the set of databased queries to the set of databases, which implements the updates to the ontology. The system is then programmed to receive data query results and transmit them in response to the specific user query.