Graph-Based Natural Language Optimization for Operating Manual Information Gaps
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Solution Overview
Problem
Traditional operating manuals often contain information gaps, such as missing component names, operating methods, or specification conditions, leading to increased cognitive load and operating errors for users.
Innovation Solution
A graph-based natural language optimization method that extracts domain-related entities from an input sentence, analyzes their connection relationships using a graph database, and integrates filling data to generate an optimized sentence with complete information, thereby eliminating information gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional operating manuals are used, then the structure is simple and easy to produce, but information gaps exist leading to increased cognitive load and operating errors
Solution Approach 1:
A graph database serves as an intermediary between the input sentence and the filling data. The system extracts entities from the input sentence, queries the graph database to analyze connection relationships between entities, and retrieves relevant filling data. This intermediary structure enables automatic information completion without requiring complex manual editing processes, thus improving information completeness while maintaining production efficiency.
Solution Approach 2:
The patent replaces manual information verification and completion processes with automated natural language processing and graph database querying. Instead of manually checking and filling information gaps in operating manuals, the system automatically extracts entities, analyzes their relationships through graph database queries, and integrates filling data. This substitution of mechanical manual processes with automated computational processes improves both information completeness and production efficiency.
2Ease of operation
If traditional operating manuals are used, then the production process is simple, but cognitive load of users increases due to information gaps
Solution Approach 1:
The system performs preliminary action by proactively identifying and filling information gaps before users encounter them. When processing an input sentence, the system extracts entities, queries the graph database to find related information, and automatically integrates filling data into the output sentence. This preliminary completion of information ensures that users receive comprehensive operating instructions without having to search for missing details, thereby reducing cognitive load and improving ease of operation.
Solution Approach 2:
The system implements feedback by using the graph database to analyze connection relationships between entities extracted from the input sentence. The graph database structure allows the system to trace relationships between entities and retrieve relevant filling data that connects back to the original context. This feedback mechanism ensures that the generated output sentence contains complete and contextually appropriate information, improving user understanding without requiring additional manual intervention.
3Reliability
If information gaps are filled manually, then information completeness improves, but time consumption and productivity decrease
Solution Approach 1:
The system enables self-service by automatically completing information gaps without requiring manual intervention. The input sentence is processed through automated entity extraction, graph database querying for relationship analysis, and automatic integration of filling data. This self-service mechanism allows the system to generate complete output sentences autonomously, maintaining high information completeness while preserving processing efficiency and productivity.
Solution Approach 2:
The patent transforms the processing approach by changing parameters from manual text editing to automated computational processing. The system converts the input sentence into structured entities, queries the graph database using these entities as parameters, and automatically integrates results. This parameter-based automated processing approach maintains information completeness while dramatically improving processing efficiency compared to manual methods.
Data Source
AI summary
A graph-based natural language optimization method and an electronic apparatus are provided. The method is adapted for an electronic apparatus with a processor. In the method, an input sentence submitted by a user is received, and multiple domain-related entities are extracted from the input sentence. The domain-related entities are input to a graph database to analyze a connection relationship between the domain-related entities, and filling data is obtained from the graph database based on the connection relationship. The input sentence and the filling data are integrated via a natural language processing technology to generate an optimized natural language sentence.