Concept Classification Disambiguation Using Clue Concept Index
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Solution Overview
Problem
Current artificial intelligence and analytics systems face challenges in automatically disambiguating the semantic meaning of data items, leading to inaccurate recommendations and user dissatisfaction due to the inability to classify concepts accurately using language-specific clues.
Innovation Solution
A method and system that utilize natural language processing to generate additional lexical information for data item labels, build search queries, and search a clue concept index to select the most relevant concept, incorporating a custom scoring algorithm to enhance accuracy and eliminate ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is used to analyze data item labels and search clue concept index, then concept classification accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by building a clue concept index in advance that contains pre-extracted linguistic clues and concepts. When classifying a data item, the system searches this pre-built index rather than performing full semantic analysis from scratch, significantly reducing processing complexity while maintaining high classification accuracy.
Solution Approach 2:
The concept classification process is segmented into distinct modular steps: (1) extracting linguistic clues from data item labels using NLP, (2) building search queries from these clues, (3) searching the clue concept index, and (4) selecting the best matching concept. This segmentation allows each module to be optimized independently and reduces overall system complexity.
2Measurement precision
If natural language processing tools are used to generate lexical information, then concept disambiguation accuracy is improved, but processing time increases
Solution Approach 1:
The clue concept index is built in advance using NLP tools to extract and store linguistic clues, concepts, and their relationships. This preliminary processing converts complex semantic analysis into a structured index that can be quickly searched, reducing real-time processing time while preserving disambiguation accuracy.
Solution Approach 2:
Instead of performing full semantic analysis on each data item, the system creates simplified copies of conceptual information in the clue concept index. These copies contain essential linguistic patterns and concept associations that can be matched quickly without requiring complete re-analysis of the original data structures.
Data Source
AI summary
A computer-implemented method, system, and computer program product for classifying a concept of a data item. A data item label for the data item is received. The data item label is analyzed using a natural language processing tool to generate additional lexical information for the data item label. A search query is built using the additional lexical information for the data item label. The search query is used to search a clue concept index to produce a search result. The clue concept index comprises clue concept records that identify clues for concepts. The search result identifies clue concept records from the clue concept index that match the search query. A concept is selected from the clue concept records identified in the search result as the concept for the data item.