Natural Language Graph Querying for Cybersecurity Repositories
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Solution Overview
Problem
Cybersecurity analysts face challenges in efficiently querying cybersecurity data repositories due to the need to learn multiple graph query languages and interpret results in unfamiliar formats, which are time-consuming and inefficient.
Innovation Solution
Systems and methods that allow users to query graph databases using natural language queries, converting them into graph-specific queries and explaining results in natural language using large language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If analysts use graph query languages to query cybersecurity data repositories, then they can obtain information from the databases, but they need to spend time learning the structure and syntax of multiple different graph query languages
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates between natural language queries and graph database query languages. This mediator handles the complexity of multiple graph query languages internally, allowing analysts to interact with diverse databases using a single natural language interface without needing to learn different query languages.
Solution Approach 2:
The system creates a universal natural language interface that works across multiple different graph database repositories. Instead of requiring separate query languages for each database, the system provides a single multi-functional interface that can query various cybersecurity data repositories uniformly, eliminating the need to learn multiple query languages.
2Adaptability or versatility
If analysts query multiple independent data repositories with different features and data structures, then they can locate desired information, but they need to understand how to leverage multiple different graph query languages
Solution Approach 1:
The natural language processing system acts as an intermediary layer between the user and multiple independent data repositories. It handles the complexity of different data structures and query languages internally, translating natural language requests into appropriate queries for each repository while presenting a unified interface to the analyst.
Solution Approach 2:
The system implements a universal query interface that can adapt to multiple independent repositories with different features and data structures. This single interface performs the function of multiple specialized query languages, allowing analysts to query diverse repositories without learning their individual query languages.
3Measurement precision
If graph databases return results in graph-specific terminology and syntax, then they provide accurate query results, but analysts find the results unfamiliar and difficult to understand
Solution Approach 1:
The system introduces a natural language generation intermediary that translates graph database results from graph-specific terminology and syntax back into natural language. This mediator preserves the accuracy of the query results while making them accessible and understandable to analysts who may not be familiar with graph database terminology.
Solution Approach 2:
Instead of requiring analysts to interpret results in graph database terminology, the system inverts the translation process by converting results from graph-specific language back into natural language. This inversion makes the results familiar and easy to understand while maintaining the precision of the underlying graph query results.
Data Source
AI summary
A method for querying a graph database using natural language queries comprises: receiving a natural language user query; identifying one or more node types from a type graph in the natural language user query, wherein the one or more node types correspond to one or more words or phrases in the natural language user query; generating, using a large language model, a graph database query based on the one or more node types identified in the natural language user query; querying a graph database using the graph database query generated by the large language model; receiving results of the graph database query; and generating, using the large language model, a natural language response to the natural language user query based on the results of the graph database query.