Natural Language Graph Query Guidance System
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Solution Overview
Problem
Non-technical users find it difficult to explore and visualize graph data due to the complexity of graph database query languages and APIs, limiting their ability to gain insights from graph databases.
Innovation Solution
A system that uses natural language processing and guidance algorithms to enable non-technical users to interact with graph databases through a familiar interface, allowing them to specify queries in plain language and automatically convert them into equivalent graph database query language, eliminating the need to understand complex syntax and grammar.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If graph database query languages and APIs are used to explore graph data, then data exploration capability is improved, but user accessibility deteriorates due to complexity
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into graph database query language. This mediator system includes a language model trained on graph query patterns, a query generation module, and a result visualization component, allowing non-technical users to interact with complex graph databases through simple language without needing to learn specialized query syntax
Solution Approach 2:
The system segments the complex query process into distinct components: natural language input processing, intent recognition, query template selection, parameter extraction, and result visualization. Each component handles a specific aspect of the translation process, breaking down the complexity so that users only need to provide natural language input while the system manages the sophisticated query construction and execution
2Measurement precision
If graph database query language is used, then query precision is improved, but learning curve increases for non-technical users
Solution Approach 1:
The system creates and maintains a library of pre-defined query templates that replicate common graph exploration patterns. These templates are automatically selected and customized based on the user's natural language input, allowing the system to generate precise queries without requiring users to learn query syntax. The templates capture complex graph traversal patterns, relationship filtering, and aggregation logic that would otherwise require extensive technical knowledge
3Ease of operation
If natural language interface is provided, then ease of operation is improved, but query accuracy may deteriorate without proper guidance
Solution Approach 1:
The system implements feedback mechanisms where the language model continuously learns from query patterns and user interactions. The system provides feedback to users about query interpretation, asks clarifying questions when ambiguity is detected, and refines query generation based on result analysis. This feedback loop ensures that natural language inputs are accurately translated into precise graph queries while maintaining ease of use
Data Source
AI summary
Techniques are disclosed to provide guided natural language interactions with a connected data system, such as a graph database. In various embodiments, natural language input associated with a graph database query associated with a graph data set is received. The natural language input is processed to generate a set of candidate strings. At least a subset of the candidate strings is mapped to an entity in the graph data set. The entity and data comprising the graph data are used set to determine a candidate graph pattern associated with the graph database query. The candidate graph pattern is used to guide a user associated with the natural language input to refine the graph database query.