Canonical Data Model Entity Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Heterogeneous information systems used by businesses often lead to misinterpretation and inconsistent information exchange due to differences in proprietary or third-party-defined messaging standards, resulting in potential manufacturing faults and financial losses.
Innovation Solution
A method for consistently labeling business entities in a canonical data model by generating natural-language names that are descriptive, discriminative, and semantically unique, using a two-step process involving candidate phrase generation and ranking, and applying constraint satisfaction to ensure uniqueness across entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If businesses use proprietary or third-party-defined messaging standards with different naming conventions, then each business can maintain its own information system structure, but misinterpretation and inconsistent information exchange occur between partners
Solution Approach 1:
The patent introduces a canonical data model as an intermediary layer between heterogeneous business information systems. This canonical model serves as a mediator that translates and standardizes data from different proprietary schemas, enabling accurate information exchange while allowing each business to maintain its own internal structure. The canonical model acts as the mediating entity that resolves naming convention conflicts without requiring changes to existing business systems.
2Ease of operation
If businesses exchange electronic messages using different schemas, then each business maintains independence, but the result is misinterpretation and inconsistent information
Solution Approach 1:
The patent creates a universal canonical data model that can handle multiple different business schemas simultaneously. This universal model serves multiple functions: it receives data from various proprietary schemas, standardizes them into a common format, and enables consistent information exchange. The canonical model's multi-functionality allows it to adapt to different business independence requirements while ensuring information consistency across all participants.
3Measurement precision
If manual intervention is used for data mapping between different schemas, then accuracy can be maintained, but computational resources and time are consumed
Solution Approach 1:
The patent implements self-service through automated label generation and constraint satisfaction algorithms. The system automatically generates candidate labels for canonical entities, ranks them based on quality criteria, and selects optimal labels without requiring manual intervention. The constraint satisfaction mechanism automatically ensures uniqueness and consistency of labels across the canonical model, eliminating the need for manual data mapping while maintaining high accuracy through algorithmic precision.
4Productivity
If automated labeling is implemented without constraint satisfaction, then processing speed increases, but identical or synonymous labels may be assigned to different entities
Solution Approach 1:
The patent implements feedback through a constraint satisfaction mechanism that continuously monitors and validates label assignments. The system generates candidate labels automatically, then applies constraint satisfaction rules that provide feedback on label quality, uniqueness, and semantic appropriateness. This feedback loop ensures that labels meet precision requirements while maintaining automated processing speed, as the constraint checking is performed algorithmically rather than manually.
Data Source
AI summary
Enterprises express the concepts of their electronic business-to-business (B2B) communication in differently structured ontology-like schemas. Collaborations benefit from merging the common concepts into semantically unique Business Entities (BEs) in a merged schema. Methods and systems for labeling the merged schema with descriptive, yet short and unique names, are described. A heuristically ranked list of descriptive candidate phrases for each BE is derived locally from the names and descriptions of the underlying concepts. A semantically unique candidate phrase is assigned to each BE that discriminates it from the other BEs by employing a constraint satisfaction problem solver.