Text Parsing for Knowledge Map Topic Creation
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Solution Overview
Problem
Current knowledge mapping tools lack a convenient method to quickly or automatically define topics and their relationships, as well as add information content to existing topics or their links in knowledge trees or networks.
Innovation Solution
A method for relational analysis of input information items, parsing titles and headers into syntactical components to determine subject and subtopic relationships, searching existing knowledge maps for matches, and storing content accordingly, enabling quick and convenient addition of information to knowledge maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual topic definition and relationship establishment is used in knowledge mapping tools, then users can precisely control topic structure and relationships, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system automatically parses input text to extract topics and relationships without requiring manual user intervention. The parsing subsystem analyzes text structure, identifies topics, and establishes relationships autonomously, allowing the system to serve itself in the knowledge mapping process.
Solution Approach 2:
The patent replaces manual mechanical operations (clicking, dragging, dropping to create topics and relationships) with automated text parsing and analysis. The parsing subsystem uses computational methods to extract topics and relationships from text, substituting manual mechanical processes with automated information processing.
2Productivity
If automated text parsing is implemented to quickly create topics and relationships, then productivity increases, but the complexity of the system increases
Solution Approach 1:
The parsing subsystem is divided into separate functional components: text analysis module, topic extraction module, and relationship identification module. Each component handles a specific aspect of the parsing process, making the overall system more manageable and easier to implement despite the automated functionality.
3Ease of operation
If existing topics are automatically matched and formatted from parsed input, then the convenience of adding information increases, but the precision of topic matching may decrease
Solution Approach 1:
The system compares parsed topics against existing topics in the knowledge map and provides feedback on matches. When matches are found, the system formats the parsed information to align with existing topic structures, automatically integrating new information while maintaining consistency with the existing knowledge map.
Data Source
AI summary
A method for performing relational analysis of parsed input is employed to create a visual map of knowledge information. A title, header or subject line for an input item of information is parsed into syntactical components of at least a subject component and any predicate component(s) relationally linked as topic and subtopics. A search of indices for the knowledge map and its topics and subtopics is carried out for the subject component. If a match is found, then the subject component is taken as the existing topic. If no match is found, then the subject component is formatted as a new entry in the knowledge map. Topic-related information content is stored in the repository referenced to the formatted topic. A similar process can be carried out for formatting predicate component(s). In this manner, input items of information can be quickly and conveniently added to the knowledge information map.