Fuzzy Term Partition Identification for Context-Aware Cognitive Computing
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Solution Overview
Problem
Existing technologies fail to accurately identify and map fuzzy terms used in everyday conversations to their corresponding crisp values, as these values can vary significantly based on context such as location, culture, and personal preferences, leading to inconsistent interpretations.
Innovation Solution
A method and system for building and applying fuzzy term partitions, which involves receiving fuzzy term inputs, building a fuzzy category taxonomy, implementing a fuzzy category classifier, and creating a fuzzy term extractor to associate fuzzy terms with context data, thereby producing context data partitions and applying weights to extracted fuzzy terms for accurate mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fuzzy terms are mapped to fixed crisp values, then the system is simple to operate, but the interpretation becomes inconsistent across different contexts
Solution Approach 1:
The patent applies dynamics by making the crisp value mapping adaptive rather than fixed. The system dynamically adjusts the mapping between fuzzy terms and crisp values based on extracted context data, allowing the same fuzzy term to map to different crisp values in different contexts while maintaining operational simplicity through automated context-aware resolution
Solution Approach 2:
The patent changes the parameter of crisp value assignment from static to context-dependent. By extracting context data and using it to determine appropriate crisp values, the system allows parameters (crisp values) to change based on contextual conditions, resolving the contradiction between simple operation and consistent interpretation
2Measurement precision
If context data is extracted and analyzed for each fuzzy term, then the interpretation accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the fuzzy term processing system into distinct functional modules: fuzzy term extractor, context data extractor, and mapping engine. This segmentation allows each component to handle specific tasks independently, improving interpretation accuracy through specialized processing while managing system complexity through modular architecture
Solution Approach 2:
The patent introduces context data as an intermediary between fuzzy terms and crisp values. This intermediary layer enables accurate interpretation by providing contextual information that bridges the gap between ambiguous fuzzy terms and precise crisp values, while the automated extraction and processing of this intermediary data manages the complexity through systematic handling
3Adaptability or versatility
If multiple context data partitions are created for different fuzzy terms, then the adaptability to different contexts is improved, but the data processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing context data into context data partitions before they are needed for fuzzy term resolution. This advance preparation allows the system to quickly retrieve and apply appropriate context partitions when processing fuzzy terms, improving adaptability while reducing real-time processing time through proactive data organization
Data Source
AI summary
A method, computer system, and a computer program product for building and applying fuzzy term partitions is provided. The present invention may include building a fuzzy category taxonomy. The present invention may also include implementing the built fuzzy category taxonomy into a fuzzy category classifier. The present invention may then include building a fuzzy term extractor. The present invention may further include building a fuzzy term association map. The present invention may also include processing a plurality of words stored on a database. The present invention may then include extracting a fuzzy term from the processed plurality of words. The present invention may further include associating the extracted fuzzy term with a plurality of context data. The present invention may also include producing a context data partition for the extracted fuzzy term. The present invention may then include applying a weight to the extracted fuzzy term.