Graphical Query Structure for Context-Based Response Accuracy
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Solution Overview
Problem
Existing question answering systems fail to consider the domain-based semantic role of each word in user queries and often miss essential words, leading to incorrect responses due to unstructured data and limited scope of training, which affects their ability to understand user natural utterances and context.
Innovation Solution
A method and system that generate a graphical structure for user queries using nodes and edges associated with predefined indexing IDs, comparing these structures to identify related predefined structures in a training database to provide context-based responses, and using Hidden Markov Models to complete queries by identifying and appending missing words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing Q&A systems use unstructured data and limited scope training, then system complexity is reduced, but response accuracy and context understanding deteriorate
Solution Approach 1:
The system segments the query processing into distinct components: generating a current graphical structure from the user query, comparing it with predefined graphical structures, and ranking matches based on node correspondence. This segmentation allows each component to be optimized independently, improving response accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces a graphical structure dimension with nodes and edges that represents semantic relationships. By mapping query words to nodes and their relationships to edges, the system adds a structural dimension to text processing, enabling more accurate context understanding while maintaining manageable complexity through standardized graph operations.
2Productivity
If existing systems use semantic labeling and linguistic analysis, then processing speed is improved, but understanding of domain-based semantic roles and missing words deteriorates
Solution Approach 1:
The system performs preliminary action by generating a current graphical structure from the user query before comparison. This structure includes nodes for each word with their semantic roles and edges representing relationships. By preparing this structured representation in advance, the system enables efficient comparison with predefined structures while maintaining high semantic understanding accuracy.
Solution Approach 2:
The graphical structure acts as an intermediary between the raw user query and the predefined question structures. It translates natural language into a standardized format with nodes and edges, preserving semantic roles and relationships. This intermediary representation enables accurate matching while maintaining processing efficiency through structured comparison operations.
Data Source
AI summary
The present disclosure relates to method and system for providing context-based response for a user query. The method comprises generating current graphical structure for user query received from user which comprises one or more nodes corresponding to one or more words in the user query and one or more edges connecting the one or more nodes. Each node in the current graphical structure is associated with predefined indexing identification (ID) which is compared with predefined indexing IDs of one or more predefined graphical structures to identify one or more related predefined graphical structures. The one or more related predefined graphical structures are ranked based on number of predefined indexing IDs being matched. Based on comparison, system provides response corresponding to predefined question associated with related predefined graphical structure with highest rank. Therefore, present disclosure provides an efficient mechanism for providing context-based response to user queries.