Document Relation Graph Time Sequentialization
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Solution Overview
Problem
Professionals face challenges in efficiently extracting relevant information from massive documents with varying creation dates and authors, as the needed knowledge is often scattered and not easily accessible, leading to time-consuming searches and a lack of capable service providers.
Innovation Solution
A system for identifying, associating, and presenting documents based on time sequentialization, utilizing a document classification storage management platform, a document library platform, and client access devices, which classifies documents, establishes inter-document relation graphs, and presents information in a chronological sequence, facilitating efficient knowledge retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professionals search through massive documents manually, then they can find the needed knowledge, but it is extremely time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically classifying documents into categories and subcategories, establishing inter-document relation graphs, and organizing knowledge points before users need to search. This preprocessing enables rapid retrieval without manual browsing through massive documents.
Solution Approach 2:
The patent introduces an intermediary system (the document management system with automated classification and relation graph construction) that mediates between the massive document collection and the user's search query. This intermediary pre-organizes information using algorithms, reducing the time users need to spend on manual information extraction.
2Ease of operation
If documents are organized by traditional methods, then storage is simple, but finding scattered knowledge across multiple documents is difficult
Solution Approach 1:
The patent merges multiple documents into an inter-document relation graph that shows relationships between knowledge points across different documents. This combining approach allows the system to present scattered knowledge in an integrated view, making it easier to find connections between concepts that span multiple source documents.
Solution Approach 2:
The system adds a new dimension to document organization by creating relation graphs that map relationships between knowledge points across documents. This dimensional transformation from simple storage to relational mapping enables users to navigate and discover scattered knowledge through visual and structural relationships rather than linear searching.
3Adaptability or versatility
If a comprehensive document system is built, then all knowledge is available, but the system complexity increases
Solution Approach 1:
The patent segments the comprehensive document system into distinct functional modules: document classification, inter-document relation graph construction, and knowledge point extraction. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining comprehensive knowledge coverage through coordinated module operations.
Data Source
AI summary
The present invention discloses a system for identifying, associating, searching and presenting documents based on time sequentialization, which builds a computer-based knowledge management system for a professional field and improves the learning efficiency and utilization of professional field knowledge. The technical solution of the present invention includes finding a series of documents having an inter-document logical relationship with a group of specific keywords from massive documents in a keyword search manner, and naming a relation graph among the specific series of documents with a group of keywords strongly correlated therewith, and presenting the inter-document relation graph in time sequence in accordance with evolved versions; combining into one set a plurality of inter-document relation graphs of which the names have a group of like term keywords and which have specific logical relationships among one another, and naming the same with the group of like term keywords in a certain logic order. On this basis, the inter-document relation graphs mentioned above are presented from multiple perspectives and are presented in multiple layers by means of graphicalization, time sequentialization and a set sequence based on a specific logical relationship in a manner complying with the logic of human brain thinking process.