Knowledge Management System Using Game Theory for Credible Information Classification
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Solution Overview
Problem
Conventional information portals and knowledge management systems fail to effectively guide knowledge acquisition, building, and sharing due to lack of credibility in information referral, inefficiencies in converting information artifacts to knowledge artifacts, and inadequate methods for rapid knowledge evolution and reorganization.
Innovation Solution
A knowledge management system utilizing natural language, game theory, and social networking methods to create a structured environment for knowledge classification, credibility assessment, and sharing through Interest Groups, Classification Contexts, and Position Moves, with Ontological Lexicons and game algorithms to guide decision-making and knowledge artifact creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional information portals use paid advertising for information product selection, then advertising revenue is generated, but information credibility deteriorates
Solution Approach 1:
The patent introduces a dual-layer system where an intermediary evaluation mechanism separates advertising revenue generation from information selection. Professional evaluators and community members act as intermediaries who assess information quality independently of advertising interests, thereby maintaining credibility while allowing paid advertising to fund the system.
Solution Approach 2:
The patent segments the information portal into distinct functional layers: advertising revenue generation, professional evaluation, community evaluation, and information delivery. This segmentation allows advertising to fund the system without directly influencing information selection, resolving the contradiction between revenue generation and credibility maintenance.
2Reliability
If multiple information artifacts are collected and organized to create knowledge artifacts, then knowledge completeness is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary classification and tagging of information artifacts as they are collected, organizing them into structured categories before the knowledge artifact creation process begins. This preliminary organization reduces the time required for subsequent processing and synthesis of multiple artifacts into complete knowledge artifacts.
Solution Approach 2:
The patent replaces manual, mechanical processes of collecting and organizing information artifacts with automated computational systems that use natural language processing and machine learning to rapidly classify, tag, and synthesize multiple artifacts into knowledge artifacts, significantly reducing processing time while maintaining completeness.
3Reliability
If knowledge artifacts are published through traditional gateways, then quality control is maintained, but knowledge evolution speed deteriorates
Solution Approach 1:
The patent implements a dynamic publishing system with multiple pathways: traditional expert review for high-stakes knowledge artifacts, community review for moderate-importance artifacts, and rapid publication for time-sensitive information. This dynamic approach allows quality control mechanisms to adapt to the urgency and importance of different knowledge artifacts, maintaining quality while enabling rapid evolution when needed.
Solution Approach 2:
The patent merges traditional expert review processes with modern community-based evaluation and automated quality assessment systems. This combination allows multiple quality control mechanisms to work together, providing both rigorous review when necessary and rapid publication when appropriate, thus maintaining quality control while accelerating knowledge evolution.
4Ease of operation
If free-form repositories are used for knowledge sharing, then ease of contribution is improved, but knowledge organization deteriorates
Solution Approach 1:
The patent implements self-service automated classification and tagging systems that automatically organize contributed knowledge artifacts as they are added to the repository. Users contribute freely without manual classification, while the system automatically processes and organizes the content using natural language processing and machine learning, thus maintaining ease of contribution while improving knowledge organization.
Data Source
AI summary
A system and method of guiding knowledge management including a knowledge artifact guiding system server including an ontological lexicon, a game rule base and interest group management. The system further include a knowledge artifact guiding system client including a guided context processor subsystem with a contextual processor and a post contextual processor, and a human computer interface with a natural language handler, a game display algorithm engine and a game position move adapter. The ontological lexicon is configured to provide lexicon updates to the knowledge artifact guiding system server and the interest group management is configured to provide position data to the knowledge artifact guiding system server. The guided contextual processor subsystem and the knowledge artifact guiding system server share knowledge artifact classification contexts and suggested knowledge artifacts. The human computer interface subsystem and the knowledge artifact guiding system server share human computer interface position data.