Ontological Subject Maps for Knowledge Discovery Efficiency
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Solution Overview
Problem
Current methods of research, learning, and knowledge discovery using informational retrieval systems and large data set analysis are inefficient, requiring significant expertise and time, and lack a systematic way to build databases of verified facts, leading to overlooked important subjects and a need for measuring the importance of knowledge compositions.
Innovation Solution
The development of a system that uses ontological subject maps (OSMs) and association strength measures to identify and rank the importance of ontological subjects within a composition, enabling machines to process and analyze vast amounts of data to extract relevant information and generate new knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If informational retrieval systems and large data set analysis are used for knowledge discovery, then the quantity of information processed increases, but the efficiency and time required deteriorate
Solution Approach 1:
The patent segments the large data set into multiple partitions, each processed by separate processing units. The knowledge base is divided into multiple sections that can be independently accessed. This segmentation allows parallel processing of information while maintaining systematic organization, resolving the contradiction between processing quantity and efficiency.
Solution Approach 2:
The patent creates a universal knowledge representation system that can handle multiple types of data and queries through a common framework. The ontological subject mapping system provides multi-functional capabilities for knowledge acquisition, retrieval, and validation across different domains, improving efficiency while processing diverse information quantities.
2Ease of operation
If traditional information retrieval methods are used, then expertise requirements increase, but the system complexity decreases
Solution Approach 1:
The patent introduces an intermediary ontological mapping system that translates complex knowledge relationships into structured, machine-processable formats. This intermediary layer handles the complexity of knowledge representation internally while providing simplified access interfaces, reducing the expertise required for users while managing system complexity through standardized mappings.
Solution Approach 2:
The system implements self-service mechanisms through automatic ontological subject mapping and knowledge base construction. The system autonomously processes information, validates facts, and organizes knowledge without requiring extensive user expertise, thereby easing operation while the underlying complexity is managed through automated algorithms.
3Reliability
If comprehensive data analysis is performed to avoid overlooking important subjects, then the time required increases, but the reliability of knowledge discovery improves
Solution Approach 1:
The patent performs preliminary actions by pre-processing data into structured partitions and pre-establishing ontological mappings before actual knowledge discovery queries. Important subjects and relationships are identified and organized in advance, allowing faster and more reliable discovery without overlooking critical information during the analysis phase.
Solution Approach 2:
The patent replaces manual, time-consuming analysis mechanisms with automated computational systems that process data through defined algorithms. The mechanical process of comprehensive review is substituted with systematic automated analysis using ontological mappings, maintaining reliability while significantly reducing the time required for knowledge discovery.
4Manufacturing precision
If systematic databases of verified facts are built, then the manufacturing precision of knowledge increases, but the device complexity increases
Solution Approach 1:
The patent changes the parameters of knowledge representation by transforming unstructured or semi-structured information into standardized ontological formats with defined properties and relationships. This parameter transformation enables precise knowledge representation through consistent data structures, while the systematic approach to parameter changes manages the complexity of the construction process.
Data Source
AI summary
The present invention discloses methods, systems, and tools for knowledge processing and visualizations by building various data structures corresponding to uncovered informational data such as values of association strengths or significance measures and maps of ontological subjects of compositions or one or more content accompanying a request for service by a user. In one embodiment of the invention the method assigns and calculates an ontological subject association strength/value measures and spectrums to each composition or ontological subjects of the composition. The resulting data, spectrums, and the adjacency matrix of the map or visualization are used to evaluate the merits of the compositions in the context of reference universes. It is also used as a research guiding tool for knowledge discovery or automatically generating high value compositions or new or less known knowledge about the ontological subjects of the universe. The invention serves knowledge seekers, knowledge creators, intelligent machines or robots, inventors, discoverer, as well as general public by assisting and guiding users to assess their work, identify their unknowns, optimize their research trajectory, and provide higher quality content. The method and system, thereby, is instrumental in increasing the speed and efficiency of knowledge acquisition, machine learnings, autonomous decision making, navigations, discovery, retrieval, as well as faster learning and problem solving.