Concept Attribute Determination via Onomasticon Mapping
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Solution Overview
Problem
Current formal concept analysis methods lack effective means to determine and expand concept attributes for linguistic terms, leading to inaccuracies and limitations in semantic disambiguation and concept type validation.
Innovation Solution
A system and method that determine concept attributes by identifying word senses and conceptually similar terms, generating mappings, and utilizing ontologies and logic engines to validate and store these attributes in an onomasticon for improved formal concept analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional formal concept analysis methods are used, then the process is simple, but the accuracy of concept attribute determination is insufficient
Solution Approach 1:
The patent segments the concept analysis process into distinct modules: word sense determination, conceptually similar term identification, attribute determination, and mapping generation. This segmentation allows each module to be optimized independently, improving overall accuracy while managing complexity through structured decomposition of the analysis workflow.
Solution Approach 2:
The patent introduces an onomasticon as an intermediary knowledge base that stores and manages concept attributes and their mappings. This intermediary structure enables systematic organization of linguistic knowledge, improving attribute determination accuracy by providing a centralized repository that can be queried and updated without complicating the core analysis process.
2Reliability
If concept attributes are expanded for more terms, then semantic disambiguation improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary determination of word senses and conceptually similar terms before full attribute analysis. By pre-identifying relevant linguistic entities and their relationships, the system reduces the computational scope for subsequent attribute determination, improving semantic disambiguation accuracy while minimizing the time required for the complete analysis process.
Solution Approach 2:
The patent dynamically adjusts the depth and scope of attribute expansion based on the specific concept term being analyzed. Rather than uniformly expanding all concept attributes, the system selectively determines attributes based on the term's context and importance, improving semantic disambiguation for critical terms while reducing analysis time for less significant concepts.
3Productivity
If automated methods are used to determine concept attributes, then productivity increases, but the precision of attribute determination may decrease
Solution Approach 1:
The patent implements feedback mechanisms where the system evaluates the quality and relevance of determined concept attributes, using this information to refine subsequent attribute determination processes. The onomasticon stores validated attribute mappings that serve as feedback for improving automated determination accuracy, allowing the system to learn from previous analyses and maintain high precision while preserving automation-driven productivity.
Data Source
AI summary
In one embodiment, a method for determining concept attributes for a concept term includes receiving a concept term and determining one or more word senses for the concept term. A word sense is selected from the one or more word senses, and, based on the selected word sense, one or more conceptually similar terms for the concept term is determined. The method also includes determining that at least one of the one or more conceptually similar terms is a concept attribute for the concept term and generating a mapping to associate the concept attribute with the concept term. The mapping is stored in an onomasticon.