Verifiable Semantic Rule Building via Natural Language Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional decision support systems do not enable non-programmers to verify semantic rules for semantic data, requiring programming expertise and lacking natural language interpretation, which hinders productivity and incurs additional costs.
Innovation Solution
A method and system that receive semantic rules, determine natural language interpretations based on a predetermined structure, and allow users to modify these interpretations, generating modified semantic rules through user actions, thereby enabling verifiable semantic rule building.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional decision support systems use programming languages for semantic rules, then rule precision and system reliability are improved, but ease of operation deteriorates because non-programmers cannot read or write these rules
Solution Approach 1:
The patent introduces natural language interpretation as an intermediary layer between programming specialists and business users. The system translates semantic rules into natural language expressions that non-programmers can understand and verify, while maintaining the underlying programming precision. This mediator enables both parties to interact with the system effectively without requiring programming expertise on the user side.
Solution Approach 2:
The patent creates a natural language copy or representation of the formal semantic rules. Instead of requiring users to work directly with programming language semantics, the system generates readable natural language versions that preserve the meaning and logic of the original rules, allowing verification without modifying the core programming structure.
2Manufacturing precision
If programming specialists are required to read and write semantic rules, then manufacturing precision is improved, but productivity deteriorates due to additional costs and dependency on specialists
Solution Approach 1:
The patent empowers business users to independently create, read, and verify semantic rules using natural language interfaces. Users can directly interact with the system to modify rules based on their domain knowledge without needing programming specialists. This self-service capability eliminates dependency on external experts and accelerates the rule development process.
Solution Approach 2:
The patent changes the language parameter from programming language to natural language. By transforming the expression medium to match the user's native communication style, the system maintains rule precision while dramatically improving accessibility and productivity for non-programming business users.
3Device complexity
If conventional systems do not provide natural language interpretation, then device complexity is reduced, but ease of operation worsens because verification is unreachable for non-programmers
Solution Approach 1:
The patent adds a natural language interpretation module as an intermediary layer that translates formal semantic rules into readable expressions. This addition, while increasing system complexity, enables non-programmers to verify rules effectively. The benefit of improved usability and verification capability outweighs the cost of the additional translation layer.
Data Source
AI summary
The present disclosure relates to a method and a system for enabling verifiable semantic rule building for semantic data. In one embodiment, the system enables verification of a semantic rule associated with semantic data based on natural language interpretation of the semantic rule. The system determines the natural language interpretation of the input semantic rule based on a predetermined semantic rule structure stored in a semantic data repository. Upon determining the natural language interpretation, the user may provide one or more inputs to modify the natural language interpretation. Based on the inputs, the system generates a modified natural language interpretation and modified semantic rule thus enabling user verified semantic rule building thereby improving interoperability of decision making processes.