SBVR Inconsistency Detection via Many-Sorted Logic Translation
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Solution Overview
Problem
Traditional systems are inadequate for detecting inconsistencies in Semantics of Business Vocabulary and Business Rules (SBVR) due to their error-prone manual methods, complexity, and ambiguity in natural language representation, which hinders automatic testing and compliance checking.
Innovation Solution
A method using Many-Sorted Logic that translates SBVR rules into SMT-LIB formulas, employing clustering techniques and SMT solvers to detect inconsistencies, and extracts minimally unsatisfiable sub-formulas through the MARCO technique, providing graphical representations of SBVR vocabulary to identify inheritance and ambiguities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to detect inconsistencies in SBVR rules, then flexibility in handling natural language representation is maintained, but error rate increases and detection reliability deteriorates
Solution Approach 1:
The patent introduces an intermediary translation layer that converts natural language SBVR rules into formal logical representations (SMT-LIB formulas). This intermediary step enables automated inconsistency detection while maintaining the flexibility of natural language input, resolving the contradiction between reliability and complexity by mediating between human-readable rules and machine-checkable logic.
Solution Approach 2:
The patent replaces manual inconsistency detection (mechanical human analysis) with automated SMT solver-based detection. By substituting human cognitive processes with formal logical translation and automated solving, the system achieves higher reliability without proportionally increasing operational complexity.
2Measurement precision
If automated translation of SBVR rules into SMT-LIB formulas is implemented, then inconsistency detection accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary clustering of SBVR rules before translation into SMT-LIB formulas. By grouping related rules and vocabulary beforehand, the system reduces the overall translation and processing time while maintaining detection precision, as clustered rules can be processed more efficiently than individual rules.
Solution Approach 2:
The patent segments the SBVR rule set into clustered groups based on semantic relationships. This segmentation allows the SMT solver to process smaller, organized subsets of rules rather than the entire rule base at once, reducing computational overhead and processing time while preserving detection accuracy.
3Reliability
If clustering techniques are applied to segregate SBVR vocabulary, then semantic ambiguity detection improves, but the complexity of the translation process increases
Solution Approach 1:
The patent segments SBVR vocabulary into clustered groups based on semantic relationships and inheritance hierarchies. This segmentation enables more reliable detection of semantic ambiguities by organizing vocabulary systematically, while the automated clustering algorithm manages the translation complexity through structured processing.
Solution Approach 2:
The patent performs vocabulary clustering and segregation as a preliminary step before rule translation. By pre-organizing the vocabulary structure and identifying inheritance relationships beforehand, the system improves semantic ambiguity detection while reducing the complexity of the subsequent translation process, as the vocabulary is already structured for efficient processing.
4Reliability
If comprehensive inconsistency detection across all rules is performed, then detection completeness improves, but false positive rate may increase
Solution Approach 1:
The patent extracts minimally unsatisfiable sub-formulas from the complete set of SMT-LIB formulas. By identifying and isolating the smallest inconsistent subsets, the system achieves complete detection of all inconsistencies while improving precision by focusing on minimal problematic groups rather than reporting all possible conflicting combinations, thereby reducing false positives.
Data Source
AI summary
This disclosure relates generally to detection of inconsistencies in Semantics of Business Vocabulary and Business Rules (SBVR) rules, and, more particularly, to detecting inconsistencies in SBVR using Many-Sorted Logic. Traditional systems and methods implementing graphical and other related techniques are not sufficient to detect anomalies in semantic rules, thereby leading to a different verification approach to detect logical anomalies and errors due to quantification. Embodiment of the present disclosure overcome the limitations faced by the traditional systems and methods by translating a set of SBVR rules into a SBVR eXtensible Markup Language Metadata Interchange (XMI); transforming the SBVR XMI into one or more Satisfiability Modulo Theories Library (SMT-LIB) formulas using at least one logical structure and semantic of Many-Sorted Logic; and detecting, from the one or more SMT-LIB formulas, one or more inconsistencies in semantics of SBVR by using a SMT solver invocation.