Semantic Mind Map System for Real-Time Search Result Expansion
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Solution Overview
Problem
Existing search engines lack the ability to uncover real-time semantic network relationships between text words and co-occurrence of text words, making it difficult for users to identify relevant documents effectively.
Innovation Solution
A system for real-time expression of a semantic mind map using an association matrix with start, direct, indirect, and weakly associated nodes, along with a focus operation module, which constructs a multilevel semantic network and highlights relevant documents through superscripts, enabling users to better identify relevant documents by displaying co-occurrence relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional search engines provide document lists based on search queries, then users can obtain relevant documents, but users cannot uncover the semantic network relationship between text words in real time
Solution Approach 1:
The patent segments the search result presentation into multiple hierarchical levels: traditional document lists are divided and enriched with semantic mind maps that display word co-occurrence relationships. The semantic network is segmented into direct associations, indirect associations, and weak associations, allowing users to explore relationships at different depths without overwhelming complexity.
Solution Approach 2:
The patent adds a new dimension to traditional search results by introducing semantic mind maps that visualize word co-occurrence relationships. This transforms the flat document list into a multi-dimensional interface where users can simultaneously view document relevance and semantic relationships between terms, uncovering information that was previously hidden.
2Loss of information
If search engines segment and classify search results by features, then users can better distinguish relevant documents, but the system cannot uncover co-occurrence of text words to indicate relevant documents
Solution Approach 1:
The patent merges traditional search result display with semantic analysis by integrating word co-occurrence information directly into the search interface. The system combines document listing with visual representation of term relationships, allowing users to simultaneously access both document relevance and semantic associations without requiring separate operations or tools.
Solution Approach 2:
The patent introduces semantic mind maps as an intermediary layer between the user and the document corpus. This intermediary visualizes word co-occurrence relationships and guides users to discover relevant documents through semantic connections, making the complex task of identifying term relationships easier while preserving access to the full document collection.
3Loss of information
If the system constructs an M*N association matrix to uncover multilevel semantic network relationships, then users can identify relevant documents better, but the interface complexity increases
Solution Approach 1:
The patent implements a dynamic semantic mind map that adapts to user interactions. The system dynamically generates and updates association matrices based on user selections, allowing the interface to evolve from simple document lists to complex semantic networks only when needed. This dynamic approach presents multilevel relationships progressively, reducing initial interface complexity while preserving deep analytical capabilities.
Solution Approach 2:
The patent employs a nested structure where semantic mind maps are embedded within the search result interface. The association matrix is nested within the document list, and different levels of semantic relationships (direct, indirect, weak associations) are nested within each other, allowing users to drill down from general document relevance to specific word relationships without being overwhelmed by the full complexity at once.
Data Source
AI summary
Disclosed is a system for real-time expression of a semantic mind map and its operation method there for. The system includes an association matrix and a focus associated operation module, the association matrix is connected to the focus associated operation module. The association matrix includes a start node, a direct associated module, an indirect associated module, a weakly associated module, a superscript module, or the like. The focus associated operation module includes a focused node and focus associated nodes, or the like. When the present disclosure is applied to a search engine including a cross-database search engine, a search result service interface is in real time expanded, thus being used to help a user better identify and discover relevant documents of interest.