Document Graph Navigation for Faster Reading Comprehension
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Solution Overview
Problem
Existing methods for document reading and comprehension, such as keyword searches and structural analysis, are inefficient in finding desired information and struggle with documents of varied structures, leading to time-consuming and inaccurate information retrieval.
Innovation Solution
A document reading comprehension support system and method that creates a graph representing the document structure, allowing users to select keywords from output words or phrases, and outputs related sentences and paths between designated words or phrases, including complementary words to enhance comprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword search is used to find desired information from a document, then the search can locate sentences or paragraphs containing the keyword, but the efficiency is reduced because too many hits are obtained and it takes too much time to reach the desired information
Solution Approach 1:
The document is segmented into a structured graph where nodes represent words/phrases and edges represent relationships between them. This segmentation allows the system to navigate directly to relevant sections through graph traversal rather than scanning all keyword hits, significantly reducing time to reach desired information while maintaining search accuracy.
Solution Approach 2:
A graph structure serves as an intermediary between the keyword search and the actual document content. The graph nodes and edges act as mediators that organize and prioritize search results, enabling efficient navigation to relevant information without manually reviewing all keyword matches.
2Ease of operation
If a single keyword is used for search, then the search is simple to perform, but the desired information cannot be narrowed down effectively
Solution Approach 1:
The system dynamically adapts the search process by allowing users to start with a single keyword and progressively refine results through graph exploration. The graph structure enables dynamic expansion from one keyword to related words and phrases, maintaining ease of operation while improving information retrieval accuracy through multi-level navigation.
Solution Approach 2:
The graph structure is built in advance with all words, phrases, and their relationships pre-organized. This preliminary action enables the system to quickly respond to single keyword searches by immediately traversing the pre-built graph to find related information, combining simplicity with precision.
3Stability of the object's composition
If document structural analysis is performed in accordance with set rules, then the analysis method is systematic, but documents with varied structures are difficult to handle
Solution Approach 1:
The graph-based approach provides a universal structure that can accommodate various document types and formats. By representing words, phrases, sentences, and paragraphs as nodes with flexible relationship edges, the system maintains consistent analysis methodology while adapting to diverse document structures, achieving both stability and versatility.
Solution Approach 2:
The system changes the structural parameters of document representation by converting traditional hierarchical structures into a flexible graph structure. This parameter change allows the same analysis framework to handle varied document structures by adjusting node connections and relationships rather than requiring different analysis methods for different document types.
4Measurement precision
If table of contents or index is used to find necessary information, then the document structure is utilized, but efficiency is low when the word to be found is not used directly in the table of contents or index
Solution Approach 1:
The graph structure acts as an intermediary that bridges the gap between the table of contents/index and the actual document content. By representing semantic relationships in the graph, the system can navigate from index terms to relevant content even when exact keyword matches are absent, improving both accuracy and efficiency simultaneously.
Solution Approach 2:
The system adds a semantic relationship dimension to traditional structural navigation. Instead of relying solely on hierarchical document structure or exact keyword matching, the graph introduces relationship-based navigation that enables efficient information retrieval through semantic connections, overcoming the limitations of conventional table of contents and index methods.
Data Source
AI summary
A document reading comprehension support system which presents information necessary for a user with high accuracy is provided. A reading comprehension support system which receives a designated document; creates a first graph representing a structure of the designated document with words or phrases contained in the designated document; outputs two or more words or phrases contained in the first graph; receives a plurality of designated words or phrases from the output words or phrases; searches the first graph with the plurality of designated words or phrases; and outputs a search result, is provided. As the search result, at least a second graph representing a shortest path between any two of the plurality of designated words or phrases in the first graph can be output. The shortest path is a path connecting any two of the plurality of designated words or phrases through at least one complementary word or phrase. The complementary word or phrase is a word or phrase different from the plurality of designated words or phrases.