Atomic Knowledge Representation Model for Context-Aware Information Filtering
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Solution Overview
Problem
The sheer volume of content in digital information systems makes it challenging to determine what information is of interest to users, leading to overwhelming them with irrelevant data, and existing methods struggle to efficiently customize knowledge representations for individual users.
Innovation Solution
The implementation of an atomic knowledge representation model (AKRM) that uses a combination of elemental data structures and knowledge processing rules to create and customize knowledge representations, allowing for the identification of relevant information and personalized content delivery based on user context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines retrieve and index large numbers of web pages to provide comprehensive information, then the quantity of available information increases, but the complexity of processing and filtering this information increases
Solution Approach 1:
The patent extracts only the essential information needed for knowledge representation from the vast amount of available content. Instead of processing all web pages, the system identifies and extracts key concepts, relationships, and facts that form the core knowledge structure, thereby reducing processing complexity while maintaining information quality.
Solution Approach 2:
The knowledge representation is segmented into modular components including concepts, attributes, and relationships. This segmentation allows the system to manage large quantities of information through organized, hierarchical structures that can be processed independently, reducing overall system complexity.
2Ease of operation
If knowledge representations are customized for individual users to improve relevance, then user experience improves, but the complexity of creating and maintaining these customizations increases
Solution Approach 1:
The knowledge representation system dynamically adapts to user preferences and contexts automatically. The system modifies knowledge graphs and retrieves information based on real-time user behavior patterns, ensuring personalization without requiring manual customization complexity.
Solution Approach 2:
The system performs self-customization by automatically learning user preferences from interaction patterns. Rather than requiring manual configuration of knowledge representations for each user, the system autonomously adjusts and optimizes knowledge graphs based on observed user behavior, reducing maintenance complexity.
3Measurement precision
If the system processes and filters large volumes of information to identify relevant content, then information retrieval accuracy improves, but the time required for processing increases
Solution Approach 1:
The system performs preliminary processing by pre-indexing and pre-organizing knowledge representations before actual queries are received. This preliminary structuring of information allows for rapid retrieval and filtering during actual use, reducing processing time while maintaining high accuracy through pre-computed relationships and attributes.
Data Source
AI summary
Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.