Natural Language Requirement Translation via Grammar Graph Ontology
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Solution Overview
Problem
The process of translating natural language software requirements into machine-readable representations is costly and time-consuming, often leading to software faults due to vagueness or inconsistency in initial requirements, which can escalate development costs if not addressed early.
Innovation Solution
A system that analyzes natural language expressions to generate a structured representation using a grammar graph and ontology concepts, automating the translation process to produce a completed translation readable by both humans and machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual translation of natural language requirements to machine-readable expressions is performed, then translation accuracy can be maintained, but the process is costly and time-consuming
Solution Approach 1:
The patent replaces manual mechanical translation processes with an automated computational system that uses natural language processing techniques. The system analyzes natural language expressions, identifies entities and relationships, and generates machine-readable formal representations automatically, eliminating the need for manual translation while maintaining accuracy through structured parsing and ontology mapping.
Solution Approach 2:
The patent introduces an intermediary formal language layer between natural language requirements and machine-executable code. This formal language serves as a mediator that structures natural language expressions into standardized representations, enabling automated processing while preserving the semantic meaning and allowing for systematic translation to various target formats.
2Ease of operation
If natural language requirements are used, then ease of expression is improved, but vagueness and inconsistency lead to software faults
Solution Approach 1:
The patent segments the natural language requirement into distinct semantic components using syntactic parsing. By dividing the natural language expression into tokens, phrases, and hierarchical structures, the system can analyze each component separately for clarity and consistency, then reassemble them into a formal representation that eliminates ambiguity while preserving the original intent.
Solution Approach 2:
The patent transforms the parameters of requirement representation from natural language forms to formalized structures. By changing the representation parameters into standardized formats with defined vocabularies and grammars, the system maintains ease of expression while eliminating vagueness and inconsistency through precise parameter specifications.
3Productivity
If automated translation systems are implemented, then translation speed increases, but system complexity increases
Solution Approach 1:
The patent designs a universal translation framework that handles multiple translation tasks through a single system. The same core components - natural language processing modules, ontology databases, and formal language generators - serve multiple functions including requirement analysis, consistency checking, and generation of various formal representations, thereby achieving high productivity without proportionally increasing complexity.
Data Source
AI summary
A system, computer-readable medium, and a method including receiving a textual representation of a natural language expression for a system requirement; analyzing, by the processor, the textual representation of the natural language expression to determine a natural language object, the natural language object including the textual representation of the natural language expression and syntactic attributes derived therefrom; traversing, by the processor, a grammar graph representation of a modeling language to determine a partial translation of the natural language object, the partial translation including at least one ontology concept placeholder; determining, by the processor, ontology concepts corresponding to the at least one ontology concept placeholder to complete a translation of the textual representation of the natural language expression; and generating a record of the completed translation.